Distribution

class Distribution(*args)

Base class for probability distributions.

Notes

In OpenTURNS a Distribution maps the concept of probability distribution.

Methods

abs()

Transform distribution by absolute value function.

acos()

Transform distribution by arccosine function.

acosh()

Transform distribution by acosh function.

asin()

Transform distribution by arcsine function.

asinh()

Transform distribution by asinh function.

atan()

Transform distribution by arctangent function.

atanh()

Transform distribution by atanh function.

cbrt()

Transform distribution by cubic root function.

computeBilateralConfidenceInterval(prob)

Compute a bilateral confidence interval.

computeBilateralConfidenceIntervalWithMarginalProbability(prob)

Compute a bilateral confidence interval.

computeCDF(*args)

Compute the cumulative distribution function.

computeCDFGradient(*args)

Compute the gradient of the cumulative distribution function.

computeCharacteristicFunction(x)

Compute the characteristic function.

computeComplementaryCDF(*args)

Compute the complementary cumulative distribution function.

computeConditionalCDF(*args)

Compute the conditional cumulative distribution function.

computeConditionalDDF(x, y)

Compute the conditional derivative density function of the last component.

computeConditionalPDF(*args)

Compute the conditional probability density function.

computeConditionalQuantile(*args)

Compute the conditional quantile function of the last component.

computeDDF(*args)

Compute the derivative density function.

computeDensityGenerator(betaSquare)

Compute the probability density function of the characteristic generator.

computeDensityGeneratorDerivative(betaSquare)

Compute the first-order derivative of the probability density function.

computeDensityGeneratorSecondDerivative(...)

Compute the second-order derivative of the probability density function.

computeEntropy()

Compute the entropy of the distribution.

computeGeneratingFunction(*args)

Compute the probability-generating function.

computeInverseSurvivalFunction(prob)

Compute the inverse survival function.

computeLogCharacteristicFunction(x)

Compute the logarithm of the characteristic function.

computeLogGeneratingFunction(*args)

Compute the logarithm of the probability-generating function.

computeLogPDF(*args)

Compute the logarithm of the probability density function.

computeLogPDFGradient(*args)

Compute the gradient of the log probability density function.

computeLowerExtremalDependenceMatrix()

Compute the lower extremal dependence coefficients.

computeLowerTailDependenceMatrix()

Compute the lower tail dependence coefficients.

computeMinimumVolumeInterval(prob)

Compute the confidence interval with minimum volume.

computeMinimumVolumeIntervalWithMarginalProbability(prob)

Compute the confidence interval with minimum volume.

computeMinimumVolumeLevelSet(prob)

Compute the confidence domain with minimum volume.

computeMinimumVolumeLevelSetWithThreshold(prob)

Compute the confidence domain with minimum volume.

computePDF(*args)

Compute the probability density function.

computePDFGradient(*args)

Compute the gradient of the probability density function.

computeProbability(interval)

Compute the interval probability.

computeQuantile(*args)

Compute the quantile function.

computeRadialDistributionCDF(radius[, tail])

Compute the cumulative distribution function of the squared radius.

computeScalarQuantile(prob[, tail])

Compute the quantile function for univariate distributions.

computeSequentialConditionalCDF(x)

Compute the sequential conditional cumulative distribution functions.

computeSequentialConditionalDDF(x)

Compute the sequential conditional derivative density function.

computeSequentialConditionalPDF(x)

Compute the sequential conditional probability density function.

computeSequentialConditionalQuantile(q)

Compute the conditional quantile function of the last component.

computeSurvivalFunction(*args)

Compute the survival function.

computeUnilateralConfidenceInterval(prob[, tail])

Compute a unilateral confidence interval.

computeUnilateralConfidenceIntervalWithMarginalProbability(...)

Compute a unilateral confidence interval.

computeUpperExtremalDependenceMatrix()

Compute the upper extremal dependence coefficients.

computeUpperTailDependenceMatrix()

Compute the upper tail dependence coefficients.

cos()

Transform distribution by cosine function.

cosh()

Transform distribution by cosh function.

drawCDF(*args)

Draw the cumulative distribution function.

drawLogPDF(*args)

Draw the graph or of iso-lines of log-probability density function.

drawLowerExtremalDependenceFunction()

Draw the lower extremal dependence function.

drawLowerTailDependenceFunction()

Draw the lower tail dependence function.

drawMarginal1DCDF(marginalIndex, xMin, xMax, ...)

Draw the cumulative distribution function of a margin.

drawMarginal1DLogPDF(marginalIndex, xMin, ...)

Draw the log-probability density function of a margin.

drawMarginal1DPDF(marginalIndex, xMin, xMax, ...)

Draw the probability density function of a margin.

drawMarginal1DSurvivalFunction(...[, logScale])

Draw the cumulative distribution function of a margin.

drawMarginal2DCDF(firstMarginal, ...[, ...])

Draw the cumulative distribution function of a couple of margins.

drawMarginal2DLogPDF(firstMarginal, ...[, ...])

Draw the log-probability density function of a couple of margins.

drawMarginal2DPDF(firstMarginal, ...[, ...])

Draw the probability density function of a couple of margins.

drawMarginal2DSurvivalFunction(...[, ...])

Draw the cumulative distribution function of a couple of margins.

drawPDF(*args)

Draw the graph or of iso-lines of probability density function.

drawQuantile(*args)

Draw the quantile function.

drawSurvivalFunction(*args)

Draw the cumulative distribution function.

drawUpperExtremalDependenceFunction()

Draw the upper extremal dependence function.

drawUpperTailDependenceFunction()

Draw the upper tail dependence function.

exp()

Transform distribution by exponential function.

getCDFEpsilon()

Accessor to the CDF computation precision.

getCentralMoment(n)

Accessor to the componentwise central moments.

getCholesky()

Accessor to the Cholesky factor of the covariance matrix.

getClassName()

Accessor to the object's name.

getCopula()

Accessor to the copula of the distribution.

getCorrelation()

(ditch me?)

getCovariance()

Accessor to the covariance matrix.

getDescription()

Accessor to the componentwise description.

getDimension()

Accessor to the dimension of the distribution.

getDispersionIndicator()

Dispersion indicator accessor.

getId()

Accessor to the object's id.

getImplementation()

Accessor to the underlying implementation.

getInverseCholesky()

Accessor to the inverse Cholesky factor of the covariance matrix.

getInverseIsoProbabilisticTransformation()

Accessor to the inverse iso-probabilistic transformation.

getIsoProbabilisticTransformation()

Accessor to the iso-probabilistic transformation.

getKendallTau()

Accessor to the Kendall coefficients matrix.

getKurtosis()

Accessor to the componentwise kurtosis.

getMarginal(*args)

Accessor to marginal distributions.

getMean()

Accessor to the mean.

getMoment(n)

Accessor to the componentwise moments.

getName()

Accessor to the object's name.

getPDFEpsilon()

Accessor to the PDF computation precision.

getParameter()

Accessor to the parameter of the distribution.

getParameterDescription()

Accessor to the parameter description of the distribution.

getParameterDimension()

Accessor to the number of parameters in the distribution.

getParametersCollection()

Accessor to the parameter of the distribution.

getPearsonCorrelation()

Accessor to the Pearson correlation matrix.

getPositionIndicator()

Position indicator accessor.

getProbabilities()

Accessor to the discrete probability levels.

getRange()

Accessor to the range of the distribution.

getRealization()

Accessor to a pseudo-random realization from the distribution.

getRoughness()

Accessor to roughness of the distribution.

getSample(size)

Accessor to a pseudo-random sample from the distribution.

getSampleByInversion(size)

Accessor to a pseudo-random sample from the distribution.

getSampleByQMC(size)

Accessor to a low discrepancy sample from the distribution.

getShapeMatrix()

Accessor to the shape matrix of the underlying copula if it is elliptical.

getShiftedMoment(n, shift)

Accessor to the componentwise shifted moments.

getSingularities()

Accessor to the singularities of the PDF function.

getSkewness()

Accessor to the componentwise skewness.

getSpearmanCorrelation()

Accessor to the Spearman correlation matrix.

getStandardDeviation()

Accessor to the componentwise standard deviation.

getStandardDistribution()

Accessor to the standard distribution.

getStandardRepresentative()

Accessor to the standard representative distribution in the parametric family.

getSupport(*args)

Accessor to the support of the distribution.

hasEllipticalCopula()

Test whether the copula of the distribution is elliptical or not.

hasIndependentCopula()

Test whether the copula of the distribution is the independent one.

inverse()

Transform distribution by inverse function.

isContinuous()

Test whether the distribution is continuous or not.

isCopula()

Test whether the distribution is a copula or not.

isDiscrete()

Test whether the distribution is discrete or not.

isElliptical()

Test whether the distribution is elliptical or not.

isIntegral()

Test whether the distribution is integer-valued or not.

ln()

Transform distribution by natural logarithm function.

log()

Transform distribution by natural logarithm function.

setDescription(description)

Accessor to the componentwise description.

setName(name)

Accessor to the object's name.

setParameter(parameters)

Accessor to the parameter of the distribution.

setParametersCollection(*args)

Accessor to the parameter of the distribution.

sin()

Transform distribution by sine function.

sinh()

Transform distribution by sinh function.

sqr()

Transform distribution by square function.

sqrt()

Transform distribution by square root function.

tan()

Transform distribution by tangent function.

tanh()

Transform distribution by tanh function.

__init__(*args)
abs()

Transform distribution by absolute value function.

Returns:
distDistribution

The transformed distribution.

acos()

Transform distribution by arccosine function.

Returns:
distDistribution

The transformed distribution.

acosh()

Transform distribution by acosh function.

Returns:
distDistribution

The transformed distribution.

asin()

Transform distribution by arcsine function.

Returns:
distDistribution

The transformed distribution.

asinh()

Transform distribution by asinh function.

Returns:
distDistribution

The transformed distribution.

atan()

Transform distribution by arctangent function.

Returns:
distDistribution

The transformed distribution.

atanh()

Transform distribution by atanh function.

Returns:
distDistribution

The transformed distribution.

cbrt()

Transform distribution by cubic root function.

Returns:
distDistribution

The transformed distribution.

computeBilateralConfidenceInterval(prob)

Compute a bilateral confidence interval.

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
confIntervalInterval

The confidence interval of level \alpha.

Notes

We consider an absolutely continuous measure \mu with density function p.

The bilateral confidence interval I^*_{\alpha} is the cartesian product I^*_{\alpha} = [a_1, b_1] \times \dots \times [a_d, b_d] where a_i = F_i^{-1}((1-\beta)/2) and b_i = F_i^{-1}((1+\beta)/2) for all i and which verifies \mu(I^*_{\alpha}) = \alpha.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the bilateral confidence interval at level 0.9:

>>> confInt = paramDist.computeBilateralConfidenceInterval(0.9)
computeBilateralConfidenceIntervalWithMarginalProbability(prob)

Compute a bilateral confidence interval.

Refer to computeBilateralConfidenceInterval()

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
confIntervalInterval

The confidence interval of level \alpha.

marginalProbfloat

The value \beta which is the common marginal probability of each marginal interval.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the bilateral confidence interval at level 0.9 with marginal probability:

>>> confInt, marginalProb = paramDist.computeBilateralConfidenceIntervalWithMarginalProbability(0.9)
computeCDF(*args)

Compute the cumulative distribution function.

Parameters:
Xsequence of float, 2-d sequence of float

CDF input(s).

Returns:
Ffloat, Point

CDF value(s) at input(s) X.

Notes

The cumulative distribution function is defined as:

F_{\vect{X}}(\vect{x}) = \Prob{\bigcap_{i=1}^n X_i \leq x_i},
                         \quad \vect{x} \in \supp{\vect{X}}

computeCDFGradient(*args)

Compute the gradient of the cumulative distribution function.

Parameters:
Xsequence of float

CDF input.

Returns:
dFdthetaPoint

Partial derivatives of the CDF with respect to the distribution parameters at input X.

computeCharacteristicFunction(x)

Compute the characteristic function.

Parameters:
tfloat

Characteristic function input.

Returns:
phicomplex

Characteristic function value at input t.

Notes

The characteristic function is defined as:

\phi_X(t) = \mathbb{E}\left[\exp(- i t X)\right],
            \quad t \in \Rset

OpenTURNS features a generic implementation of the characteristic function for all its univariate distributions (both continuous and discrete). This default implementation might be time consuming, especially as the modulus of t gets high. Only some univariate distributions benefit from dedicated more efficient implementations.

computeComplementaryCDF(*args)

Compute the complementary cumulative distribution function.

Parameters:
Xsequence of float, 2-d sequence of float

Complementary CDF input(s).

Returns:
Cfloat, Point

Complementary CDF value(s) at input(s) X.

Notes

The complementary cumulative distribution function.

1 - F_{\vect{X}}(\vect{x}) = 1 - \Prob{\bigcap_{i=1}^n X_i \leq x_i}, \quad \vect{x} \in \supp{\vect{X}}

Warning

This is not the survival function (except for 1-dimensional distributions).

computeConditionalCDF(*args)

Compute the conditional cumulative distribution function.

Parameters:
Xnfloat, sequence of float

Conditional CDF input (last component).

Xcondsequence of float, 2-d sequence of float with size n-1

Conditionning values for the other components.

Returns:
Ffloat, sequence of float

Conditional CDF value(s) at input X_n, X_{cond}.

Notes

The conditional cumulative distribution function of the last component with respect to the other fixed components is defined as follows:

F_{X_n \mid X_1, \ldots, X_{n - 1}}(x_n) =
    \Prob{X_n \leq x_n \mid X_1=x_1, \ldots, X_{n-1}=x_{n-1}},
    \quad x_n \in \supp{X_n}

computeConditionalDDF(x, y)

Compute the conditional derivative density function of the last component.

With respect to the other fixed components.

Parameters:
Xnfloat

Conditional DDF input (last component).

Xcondsequence of float with dimension n-1

Conditionning values for the other components.

Returns:
dfloat

Conditional DDF value at input X_n, X_{cond}.

computeConditionalPDF(*args)

Compute the conditional probability density function.

Conditional PDF of the last component with respect to the other fixed components.

Parameters:
Xnfloat, sequence of float

Conditional PDF input (last component).

Xcondsequence of float, 2-d sequence of float with size n-1

Conditionning values for the other components.

Returns:
Ffloat, sequence of float

Conditional PDF value(s) at input X_n, X_{cond}.

computeConditionalQuantile(*args)

Compute the conditional quantile function of the last component.

Conditional quantile with respect to the other fixed components.

Parameters:
pfloat, sequence of float, 0 < p < 1

Conditional quantile function input.

Xcondsequence of float, 2-d sequence of float with size n-1

Conditionning values for the other components.

Returns:
X1float

Conditional quantile at input p, X_{cond}.

computeDDF(*args)

Compute the derivative density function.

Parameters:
Xsequence of float, 2-d sequence of float

PDF input(s).

Returns:
dPoint, Sample

DDF value(s) at input(s) X.

Notes

The derivative density function is the gradient of the probability density function with respect to \vect{x}:

\vect{\nabla}_{\vect{x}} f_{\vect{X}}(\vect{x}) =
    \Tr{\left(\frac{\partial f_{\vect{X}}(\vect{x})}{\partial x_i},
              \quad i = 1, \ldots, n\right)},
    \quad \vect{x} \in \supp{\vect{X}}

computeDensityGenerator(betaSquare)

Compute the probability density function of the characteristic generator.

PDF of the characteristic generator of the elliptical distribution.

Parameters:
beta2float

Density generator input.

Returns:
pfloat

Density generator value at input X.

Notes

This is the function \phi such that the probability density function rewrites:

f_{\vect{X}}(\vect{x}) =
    \phi\left(\Tr{\left(\vect{x} - \vect{\mu}\right)}
                  \mat{\Sigma}^{-1}
                  \left(\vect{x} - \vect{\mu}\right)
        \right),
    \quad \vect{x} \in \supp{\vect{X}}

This function only exists for elliptical distributions.

computeDensityGeneratorDerivative(betaSquare)

Compute the first-order derivative of the probability density function.

PDF of the characteristic generator of the elliptical distribution.

Parameters:
beta2float

Density generator input.

Returns:
pfloat

Density generator first-order derivative value at input X.

Notes

This function only exists for elliptical distributions.

computeDensityGeneratorSecondDerivative(betaSquare)

Compute the second-order derivative of the probability density function.

PDF of the characteristic generator of the elliptical distribution.

Parameters:
beta2float

Density generator input.

Returns:
pfloat

Density generator second-order derivative value at input X.

Notes

This function only exists for elliptical distributions.

computeEntropy()

Compute the entropy of the distribution.

Returns:
efloat

Entropy of the distribution.

Notes

The entropy of a distribution is defined by:

\cE_X = \Expect{-\log(p_X(\vect{X}))}

Where the random vector \vect{X} follows the probability distribution of interest, and p_X is either the probability density function of \vect{X} if it is continuous or the probability distribution function if it is discrete.

computeGeneratingFunction(*args)

Compute the probability-generating function.

Parameters:
zfloat or complex

Probability-generating function input.

Returns:
gfloat

Probability-generating function value at input X.

See also

isDiscrete

Notes

The probability-generating function is defined as follows:

G_X(z) = \Expect{z^X}, \quad z \in \Cset

This function only exists for discrete distributions. OpenTURNS implements this method for univariate distributions only.

computeInverseSurvivalFunction(prob)

Compute the inverse survival function.

Parameters:
pfloat, p \in [0; 1]

Level of the survival function.

Returns:
xPoint

Point \vect{x} such that S_{\vect{X}}(\vect{x}) = p with iso-quantile components.

Notes

The inverse survival function writes: S^{-1}(p)  =  \vect{x}^p where S( \vect{x}^p) = \Prob{\bigcap_{i=1}^d X_i > x_i^p}. OpenTURNS returns the point \vect{x}^p such that \Prob{ X_1 > x_1^p}   =  \dots = \Prob{ X_d > x_d^p}.

computeLogCharacteristicFunction(x)

Compute the logarithm of the characteristic function.

Parameters:
tfloat

Characteristic function input.

Returns:
phicomplex

Logarithm of the characteristic function value at input t.

Notes

OpenTURNS features a generic implementation of the characteristic function for all its univariate distributions (both continuous and discrete). This default implementation might be time consuming, especially as the modulus of t gets high. Only some univariate distributions benefit from dedicated more efficient implementations.

computeLogGeneratingFunction(*args)

Compute the logarithm of the probability-generating function.

Parameters:
zfloat or complex

Probability-generating function input.

Returns:
lgfloat

Logarithm of the probability-generating function value at input X.

Notes

This function only exists for discrete distributions. OpenTURNS implements this method for univariate distributions only.

computeLogPDF(*args)

Compute the logarithm of the probability density function.

Parameters:
Xsequence of float, 2-d sequence of float

PDF input(s).

Returns:
ffloat, Point

Logarithm of the PDF value(s) at input(s) X.

computeLogPDFGradient(*args)

Compute the gradient of the log probability density function.

Parameters:
Xsequence of float

PDF input.

Returns:
dfdthetaPoint

Partial derivatives of the logPDF with respect to the distribution parameters at input X.

computeLowerExtremalDependenceMatrix()

Compute the lower extremal dependence coefficients.

We assume that the distribution is d-dimensional, with d \geq 2 and that its copula is denoted by C. The lower extremal dependence matrix is (\bar{\chi}_L^{ij})_{1 \leq i, j \leq d} where \\bar{\chi}_L^{ij} is the lower extremal dependence coefficient of the bivariate distribution (X_i, X_j). It is defined by:

\bar{\chi}_L^{ij} = \lim_{u \to 0} \bar{\chi}_L^{ij}(u)

where u \mapsto \bar{\chi}_L^{ij}(u) is the lower extremal dependence function of the bivariate distribution (X_i, X_j) defined by:

\bar{\chi}_L(u) = \frac{2 \log u}{\log C(u,u)} - 1, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
depCorrelationMatrix

The dependence matrix.

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> chiLb = copula.computeLowerExtremalDependenceMatrix()[1, 0]
computeLowerTailDependenceMatrix()

Compute the lower tail dependence coefficients.

We assume that the distribution is d-dimensional, with d \geq 2 and that its copula is denoted by C. The lower tail dependence matrix is (\chi_L^{ij})_{1 \leq i, j \leq d} where \chi_L^{ij} is the lower tail dependence coefficient of the bivariate distribution (X_i, X_j). It is defined by:

\chi_L^{ij} = \lim_{u \to 0} \chi_L^{ij}(u)

where u \mapsto \chi_L^{ij}(u) is the lower tail dependence function of the bivariate distribution (X_i, X_j) defined by:

\chi_L(u) = \frac{\log (1 - C(u,u))}{\log (1-u)}, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
depCorrelationMatrix

The dependence matrix.

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> chiL = copula.computeLowerTailDependenceMatrix()[1, 0]
computeMinimumVolumeInterval(prob)

Compute the confidence interval with minimum volume.

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
confIntervalInterval

The confidence interval of level \alpha.

Notes

We consider an absolutely continuous measure \mu with density function p.

The minimum volume confidence interval I^*_{\alpha} is the cartesian product I^*_{\alpha} = [a_1, b_1] \times \dots \times [a_d, b_d] where [a_i, b_i]   = \argmin_{I \in \Rset \, | \, \mu_i(I) = \beta} \lambda_i(I) and \mu(I^*_{\alpha})  =  \alpha with \lambda is the Lebesgue measure on \Rset^d.

This problem resorts to solving d univariate non linear equations: for a fixed value \beta, we find each intervals [a_i, b_i] such that:

\begin{eqnarray*}
F_i(b_i) - F_i(a_i) & = & \beta \\
p_i(b_i) & = & p_i(a_i)
\end{eqnarray*}

which consists of finding the bound a_i such that:

p_i(a_i) =  p_i(F_i^{-1}(\beta + F_i(a_i)))

To find \beta, we use the Brent algorithm: \mu([\vect{a}(\beta); \vect{b}(\beta)] = g(\beta) = \alpha with g a non linear function.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the confidence interval of the native parameters at level 0.9 with minimum volume:

>>> ot.ResourceMap.SetAsUnsignedInteger('Distribution-MinimumVolumeLevelSetSamplingSize', 1000)
>>> confInt = paramDist.computeMinimumVolumeInterval(0.9)
>>> ot.ResourceMap.Reload()
computeMinimumVolumeIntervalWithMarginalProbability(prob)

Compute the confidence interval with minimum volume.

Refer to computeMinimumVolumeInterval()

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
confIntervalInterval

The confidence interval of level \alpha.

marginalProbfloat

The value \beta which is the common marginal probability of each marginal interval.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the confidence interval of the native parameters at level 0.9 with minimum volume:

>>> ot.ResourceMap.SetAsUnsignedInteger('Distribution-MinimumVolumeLevelSetSamplingSize', 1000)
>>> confInt, marginalProb = paramDist.computeMinimumVolumeIntervalWithMarginalProbability(0.9)
>>> ot.ResourceMap.Reload()
computeMinimumVolumeLevelSet(prob)

Compute the confidence domain with minimum volume.

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
levelSetLevelSet

The minimum volume domain of measure \alpha.

Notes

We consider an absolutely continuous measure \mu with density function p.

The minimum volume confidence domain A^*_{\alpha} is the set of minimum volume and which measure is at least \alpha. It is defined by:

A^*_{\alpha} = \argmin_{A \in \Rset^d\, | \, \mu(A) \geq \alpha} \lambda(A)

where \lambda is the Lebesgue measure on \Rset^d. Under some general conditions on \mu (for example, no flat regions), the set A^*_{\alpha} is unique and realises the minimum: \mu(A^*_{\alpha}) = \alpha. We show that A^*_{\alpha} writes:

A^*_{\alpha} = \{ \vect{x} \in \Rset^d \, | \, p(\vect{x}) \geq p_{\alpha} \}

for a certain p_{\alpha} >0.

If we consider the random variable Y = p(\vect{X}), with cumulative distribution function F_Y, then p_{\alpha} is defined by:

1-F_Y(p_{\alpha}) = \alpha

Thus the minimum volume domain of confidence \alpha is the interior of the domain which frontier is the 1-\alpha quantile of Y. It can be determined with simulations of Y.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the confidence region of minimum volume of the native parameters at level 0.9:

>>> levelSet = paramDist.computeMinimumVolumeLevelSet(0.9)
>>> ot.ResourceMap.Reload()
computeMinimumVolumeLevelSetWithThreshold(prob)

Compute the confidence domain with minimum volume.

Refer to computeMinimumVolumeLevelSet()

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

Returns:
levelSetLevelSet

The minimum volume domain of measure \alpha.

levelfloat

The value p_{\alpha} of the density function defining the frontier of the domain.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the confidence region of minimum volume of the native parameters at level 0.9 with PDF threshold:

>>> levelSet, threshold = paramDist.computeMinimumVolumeLevelSetWithThreshold(0.9)
computePDF(*args)

Compute the probability density function.

Parameters:
Xsequence of float, 2-d sequence of float

PDF input(s).

Returns:
ffloat, Point

PDF value(s) at input(s) X.

Notes

The probability density function is defined as follows:

f_{\vect{X}}(\vect{x}) = \frac{\partial^n F_{\vect{X}}(\vect{x})}
                              {\prod_{i=1}^n \partial x_i},
                         \quad \vect{x} \in \supp{\vect{X}}

computePDFGradient(*args)

Compute the gradient of the probability density function.

Parameters:
Xsequence of float

PDF input.

Returns:
dfdthetaPoint

Partial derivatives of the PDF with respect to the distribution parameters at input X.

computeProbability(interval)

Compute the interval probability.

Parameters:
intervalInterval

An interval, possibly multivariate.

Returns:
Pfloat

Interval probability.

Notes

This computes the probability that the random vector \vect{X} lies in the hyper-rectangular region formed by the vectors \vect{a} and \vect{b}:

\Prob{\bigcap\limits_{i=1}^n a_i < X_i \leq b_i} =
    \sum\limits_{\vect{c}} (-1)^{n(\vect{c})}
        F_{\vect{X}}\left(\vect{c}\right)

where the sum runs over the 2^n vectors such that \vect{c} = \Tr{(c_i, i = 1, \ldots, n)} with c_i \in [a_i, b_i], and n(\vect{c}) is the number of components in \vect{c} such that c_i = a_i.

computeQuantile(*args)

Compute the quantile function.

Parameters:
pfloat (or sequence of float), 0 < p < 1

Quantile function input (a probability).

tailbool, optional (default=False)

Whether p should be interpreted as the complementary probability.

Returns:
XPoint (or Sample)

Quantile at probability level p.

Notes

The quantile function is also known as the inverse cumulative distribution function:

Q_{\vect{X}}(p) = F_{\vect{X}}^{-1}(p),
                  \quad p \in [0; 1]

computeRadialDistributionCDF(radius, tail=False)

Compute the cumulative distribution function of the squared radius.

For the underlying standard spherical distribution (for elliptical distributions only).

Parameters:
r2float, 0 \leq r^2

Squared radius.

Returns:
Ffloat

CDF value at input r^2.

Notes

This is the CDF of the sum of the squared independent, standard, identically distributed components:

R^2 = \sqrt{\sum\limits_{i=1}^n U_i^2}

computeScalarQuantile(prob, tail=False)

Compute the quantile function for univariate distributions.

Parameters:
pfloat, 0 < p < 1

Quantile function input (a probability).

Returns:
Xfloat

Quantile at probability level p.

See also

computeQuantile

Notes

The quantile function is also known as the inverse cumulative distribution function:

Q_X(p) = F_X^{-1}(p), \quad p \in [0; 1]

computeSequentialConditionalCDF(x)

Compute the sequential conditional cumulative distribution functions.

Parameters:
Xsequence of float, with size d

Values to be taken sequentially as argument and conditioning part of the CDF.

Returns:
Fsequence of float

Conditional CDF values at input.

Notes

The sequential conditional cumulative distribution function is defined as follows:

F^{seq}_{X_1,\ldots,X_d}(x_1,\ldots,x_d) = \left(\Prob{X_n \leq x_n \mid X_1=x_1, \ldots, X_{n-1}=x_{n-1}}\right)_{i=1,\ldots,d}

ie its n-th component is the conditional CDF of X_n at x_n given that X_1=x_1,\ldots,X_{n-1}=x_{n-1}. For n=1 it reduces to \Prob{X_1 \leq x_1}, ie the CDF of the first component at x_1.

computeSequentialConditionalDDF(x)

Compute the sequential conditional derivative density function.

Parameters:
Xsequence of float, with size d

Values to be taken sequentially as argument and conditioning part of the DDF.

Returns:
ddfsequence of float

Conditional DDF values at input.

Notes

The sequential conditional derivative density function is defined as follows:

ddf^{seq}_{X_1,\ldots,X_d}(x_1,\ldots,x_d) = \left(\dfrac{d^2}{d\,x_n^2}\Prob{X_n \leq x_n \mid X_1=x_1, \ldots, X_{n-1}=x_{n-1}}\right)_{i=1,\ldots,d}

ie its n-th component is the conditional DDF of X_n at x_n given that X_1=x_1,\ldots,X_{n-1}=x_{n-1}. For n=1 it reduces to \dfrac{d^2}{d\,x_1^2}\Prob{X_1 \leq x_1}, ie the DDF of the first component at x_1.

computeSequentialConditionalPDF(x)

Compute the sequential conditional probability density function.

Parameters:
Xsequence of float, with size d

Values to be taken sequentially as argument and conditioning part of the PDF.

Returns:
pdfsequence of float

Conditional PDF values at input.

Notes

The sequential conditional density function is defined as follows:

pdf^{seq}_{X_1,\ldots,X_d}(x_1,\ldots,x_d) = \left(\dfrac{d}{d\,x_n}\Prob{X_n \leq x_n \mid X_1=x_1, \ldots, X_{n-1}=x_{n-1}}\right)_{i=1,\ldots,d}

ie its n-th component is the conditional PDF of X_n at x_n given that X_1=x_1,\ldots,X_{n-1}=x_{n-1}. For n=1 it reduces to \dfrac{d}{d\,x_1}\Prob{X_1 \leq x_1}, ie the PDF of the first component at x_1.

computeSequentialConditionalQuantile(q)

Compute the conditional quantile function of the last component.

Parameters:
qsequence of float in [0,1], with size d

Values to be taken sequentially as the argument of the conditional quantile.

Returns:
Qsequence of float

Conditional quantiles values at input.

Notes

The sequential conditional quantile function is defined as follows:

Q^{seq}_{X_1,\ldots,X_d}(q_1,\ldots,q_d) = \left(F^{-1}(q_n|X_1=x_1,\ldots,X_{n-1}=x_{n_1}\right)_{i=1,\ldots,d}

where x_1,\ldots,x_{n-1} are defined recursively as x_1=F_1^{-1}(q_1) and given (x_i)_{i=1,\ldots,n-1}, x_n=F^{-1}(q_n|X_1=x_1,\ldots,X_{n-1}=x_{n_1}): the conditioning part is the set of already computed conditional quantiles.

computeSurvivalFunction(*args)

Compute the survival function.

Parameters:
xsequence of float, 2-d sequence of float

Survival function input(s).

Returns:
Sfloat, Point

Survival function value(s) at input(s) x.

Notes

The survival function of the random vector \vect{X} is defined as follows:

S_{\vect{X}}(\vect{x}) = \Prob{\bigcap_{i=1}^d X_i > x_i}
         \quad \forall \vect{x} \in \Rset^d

Warning

This is not the complementary cumulative distribution function (except for 1-dimensional distributions).

computeUnilateralConfidenceInterval(prob, tail=False)

Compute a unilateral confidence interval.

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

tailboolean

True indicates the interval is bounded by an lower value. False indicates the interval is bounded by an upper value. Default value is False.

Returns:
confIntervalInterval

The unilateral confidence interval of level \alpha.

Notes

We consider an absolutely continuous measure \mu.

The left unilateral confidence interval I^*_{\alpha} is the cartesian product I^*_{\alpha} = ]-\infty, b_1] \times \dots \times ]-\infty, b_d] where b_i = F_i^{-1}(\beta) for all i and which verifies \mu(I^*_{\alpha}) = \alpha. It means that \vect{b} is the quantile of level \alpha of the measure \mu, with iso-quantile components.

The right unilateral confidence interval I^*_{\alpha} is the cartesian product I^*_{\alpha} = ]a_1; +\infty[ \times \dots \times ]a_d; +\infty[ where a_i = F_i^{-1}(1-\beta) for all i and which verifies \mu(I^*_{\alpha}) = \alpha. It means that S_{\mu}^{-1}(\vect{a}) = \alpha with iso-quantile components, where S_{\mu} is the survival function of the measure \mu.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the right unilateral confidence interval at level 0.9:

>>> confInt = paramDist.computeUnilateralConfidenceInterval(0.9)

Determine the left unilateral confidence interval at level 0.9:

>>> confInt = paramDist.computeUnilateralConfidenceInterval(0.9, True)
computeUnilateralConfidenceIntervalWithMarginalProbability(prob, tail)

Compute a unilateral confidence interval.

Refer to computeUnilateralConfidenceInterval()

Parameters:
alphafloat, \alpha \in [0,1]

The confidence level.

tailboolean

True indicates the interval is bounded by an lower value. False indicates the interval is bounded by an upper value. Default value is False.

Returns:
confIntervalInterval

The unilateral confidence interval of level \alpha.

marginalProbfloat

The value \beta which is the common marginal probability of each marginal interval.

Examples

Create a sample from a Normal distribution:

>>> import openturns as ot
>>> sample = ot.Normal().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)

Fit a Normal distribution and extract the asymptotic parameters distribution:

>>> fittedRes = ot.NormalFactory().buildEstimator(sample)
>>> paramDist = fittedRes.getParameterDistribution()

Determine the right unilateral confidence interval at level 0.9:

>>> confInt, marginalProb = paramDist.computeUnilateralConfidenceIntervalWithMarginalProbability(0.9, False)

Determine the left unilateral confidence interval at level 0.9:

>>> confInt, marginalProb = paramDist.computeUnilateralConfidenceIntervalWithMarginalProbability(0.9, True)
>>> ot.ResourceMap.Reload()
computeUpperExtremalDependenceMatrix()

Compute the upper extremal dependence coefficients.

We assume that the distribution is d-dimensional, with d \geq 2 and that its copula is denoted by C. The upper extremal dependence matrix is (\bar{\chi}^{ij})_{1 \leq i, j \leq d} where \bar{\chi}^{ij} is the upper extremal dependence coefficient of the bivariate distribution (X_i, X_j). It is defined by:

\bar{\chi}^{ij} = \lim_{u \to 1} \bar{\chi}^{ij}(u)

where u \mapsto \bar{\chi}^{ij}(u) is the upper extremal dependence function of the bivariate distribution (X_i, X_j) defined by:

\bar{\chi}(u) = \frac{2 \log 1-u}{\log \bar{C}(u,u)} - 1, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
depCorrelationMatrix

The dependence matrix.

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> chib = copula.computeUpperExtremalDependenceMatrix()[1, 0]
computeUpperTailDependenceMatrix()

Compute the upper tail dependence coefficients.

We assume that the distribution is d-dimensional, with d \geq 2 and that its copula is denoted by C. The upper tail dependence matrix is (\chi^{ij})_{1 \leq i, j \leq d} where \chi^{ij} is the upper tail dependence coefficient of the bivariate distribution (X_i, X_j). It is defined by:

\chi^{ij} = \lim_{u \to 1} \chi^{ij}(u)

where u \mapsto \chi^{ij}(u) is the upper tail dependence function of the bivariate distribution (X_i, X_j) defined by:

\chi(u) = 2 - \frac{\log C(u,u)}{\log u}, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
depCorrelationMatrix

The dependence matrix.

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> chi = copula.computeUpperTailDependenceMatrix()[1, 0]
cos()

Transform distribution by cosine function.

Returns:
distDistribution

The transformed distribution.

cosh()

Transform distribution by cosh function.

Returns:
distDistribution

The transformed distribution.

drawCDF(*args)

Draw the cumulative distribution function.

Available constructors:

drawCDF(x_min, x_max, pointNumber, logScale)

drawCDF(lowerCorner, upperCorner, pointNbrInd, logScaleX, logScaleY)

drawCDF(lowerCorner, upperCorner)

Parameters:
x_minfloat, optional

The min-value of the mesh of the x-axis. Defaults uses the quantile associated to the probability level Distribution-QMin from the ResourceMap.

x_maxfloat, optional, x_{\max} > x_{\min}

The max-value of the mesh of the y-axis. Defaults uses the quantile associated to the probability level Distribution-QMax from the ResourceMap.

pointNumberint

The number of points that is used for meshing each axis. Defaults uses DistributionImplementation-DefaultPointNumber from the ResourceMap.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

lowerCornersequence of float, of dimension 2, optional

The lower corner [x_{min}, y_{min}].

upperCornersequence of float, of dimension 2, optional

The upper corner [x_{max}, y_{max}].

pointNbrIndIndices, of dimension 2

Number of points that is used for meshing each axis.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the CDF.

Notes

Only valid for univariate and bivariate distributions.

Examples

View the CDF of a univariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal()
>>> graph = dist.drawCDF()
>>> graph.setLegends(['normal cdf'])

View the iso-lines CDF of a bivariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal(2)
>>> graph2 = dist.drawCDF()
>>> graph2.setLegends(['iso- normal cdf'])
>>> graph3 = dist.drawCDF([-10, -5],[5, 10], [511, 511])
drawLogPDF(*args)

Draw the graph or of iso-lines of log-probability density function.

Available constructors:

drawLogPDF(x_min, x_max, pointNumber, logScale)

drawLogPDF(lowerCorner, upperCorner, pointNbrInd, logScaleX, logScaleY)

drawLogPDF(lowerCorner, upperCorner)

Parameters:
x_minfloat, optional

The min-value of the mesh of the x-axis. Defaults uses the quantile associated to the probability level Distribution-QMin from the ResourceMap.

x_maxfloat, optional, x_{\max} > x_{\min}

The max-value of the mesh of the y-axis. Defaults uses the quantile associated to the probability level Distribution-QMax from the ResourceMap.

pointNumberint

The number of points that is used for meshing each axis. Defaults uses DistributionImplementation-DefaultPointNumber from the ResourceMap.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

lowerCornersequence of float, of dimension 2, optional

The lower corner [x_{min}, y_{min}].

upperCornersequence of float, of dimension 2, optional

The upper corner [x_{max}, y_{max}].

pointNbrIndIndices, of dimension 2

Number of points that is used for meshing each axis.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the log-PDF or its iso_lines.

Notes

Only valid for univariate and bivariate distributions.

Examples

View the log-PDF of a univariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal()
>>> graph = dist.drawLogPDF()
>>> graph.setLegends(['normal log-pdf'])

View the iso-lines log-PDF of a bivariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal(2)
>>> graph2 = dist.drawLogPDF()
>>> graph2.setLegends(['iso- normal pdf'])
>>> graph3 = dist.drawLogPDF([-10, -5],[5, 10], [511, 511])
drawLowerExtremalDependenceFunction()

Draw the lower extremal dependence function.

We assume that the distribution is bivariate and that its copula is denoted by C. The lower extremal dependence function u \mapsto \bar{\chi}(u) is defined by:

\bar{\chi}_L(u) = \frac{2 \log u}{\log C(u,u)} - 1, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
graphGraph

The graph of the function u \mapsto \bar{\chi}_L(u).

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> graph = copula.drawLowerExtremalDependenceFunction()
drawLowerTailDependenceFunction()

Draw the lower tail dependence function.

We assume that the distribution is bivariate and that its copula is denoted by C. The lower tail dependence function u \mapsto \chi_L(u) is defined by:

\chi_L(u) = \frac{\log (1 - C(u,u))}{\log (1-u)}, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
graphGraph

Graph of the function u \mapsto \chi_L(u).

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> graph = copula.drawLowerTailDependenceFunction()
drawMarginal1DCDF(marginalIndex, xMin, xMax, pointNumber, logScale=False)

Draw the cumulative distribution function of a margin.

Parameters:
iint, 1 \leq i \leq n

The index of the margin of interest.

x_minfloat

The starting value that is used for meshing the x-axis.

x_maxfloat, x_{\max} > x_{\min}

The ending value that is used for meshing the x-axis.

n_pointsint

The number of points that is used for meshing the x-axis.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

Returns:
graphGraph

A graphical representation of the CDF of the requested margin.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal1DCDF(2, -6.0, 6.0, 100)
>>> view = View(graph)
>>> view.show()
drawMarginal1DLogPDF(marginalIndex, xMin, xMax, pointNumber, logScale=False)

Draw the log-probability density function of a margin.

Parameters:
iint, 1 \leq i \leq n

The index of the margin of interest.

x_minfloat

The starting value that is used for meshing the x-axis.

x_maxfloat, x_{\max} > x_{\min}

The ending value that is used for meshing the x-axis.

n_pointsint

The number of points that is used for meshing the x-axis.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

Returns:
graphGraph

A graphical representation of the log-PDF of the requested margin.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal1DLogPDF(2, -6.0, 6.0, 100)
>>> view = View(graph)
>>> view.show()
drawMarginal1DPDF(marginalIndex, xMin, xMax, pointNumber, logScale=False)

Draw the probability density function of a margin.

Parameters:
iint, 1 \leq i \leq n

The index of the margin of interest.

x_minfloat

The starting value that is used for meshing the x-axis.

x_maxfloat, x_{\max} > x_{\min}

The ending value that is used for meshing the x-axis.

n_pointsint

The number of points that is used for meshing the x-axis.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

Returns:
graphGraph

A graphical representation of the PDF of the requested margin.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal1DPDF(2, -6.0, 6.0, 100)
>>> view = View(graph)
>>> view.show()
drawMarginal1DSurvivalFunction(marginalIndex, xMin, xMax, pointNumber, logScale=False)

Draw the cumulative distribution function of a margin.

Parameters:
iint, 1 \leq i \leq n

The index of the margin of interest.

x_minfloat

The starting value that is used for meshing the x-axis.

x_maxfloat, x_{\max} > x_{\min}

The ending value that is used for meshing the x-axis.

n_pointsint

The number of points that is used for meshing the x-axis.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

Returns:
graphGraph

A graphical representation of the SurvivalFunction of the requested margin.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal1DSurvivalFunction(2, -6.0, 6.0, 100)
>>> view = View(graph)
>>> view.show()
drawMarginal2DCDF(firstMarginal, secondMarginal, xMin, xMax, pointNumber, logScaleX=False, logScaleY=False)

Draw the cumulative distribution function of a couple of margins.

Parameters:
iint, 1 \leq i \leq n

The index of the first margin of interest.

jint, 1 \leq i \neq j \leq n

The index of the second margin of interest.

x_minlist of 2 floats

The starting values that are used for meshing the x- and y- axes.

x_maxlist of 2 floats, x_{\max} > x_{\min}

The ending values that are used for meshing the x- and y- axes.

n_pointslist of 2 ints

The number of points that are used for meshing the x- and y- axes.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the marginal CDF of the requested couple of margins.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal2DCDF(2, 3, [-6.0] * 2, [6.0] * 2, [100] * 2)
>>> view = View(graph)
>>> view.show()
drawMarginal2DLogPDF(firstMarginal, secondMarginal, xMin, xMax, pointNumber, logScaleX=False, logScaleY=False)

Draw the log-probability density function of a couple of margins.

Parameters:
iint, 1 \leq i \leq n

The index of the first margin of interest.

jint, 1 \leq i \neq j \leq n

The index of the second margin of interest.

x_minlist of 2 floats

The starting values that are used for meshing the x- and y- axes.

x_maxlist of 2 floats, x_{\max} > x_{\min}

The ending values that are used for meshing the x- and y- axes.

n_pointslist of 2 ints

The number of points that are used for meshing the x- and y- axes.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the marginal log-PDF of the requested couple of margins.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal2DLogPDF(2, 3, [-6.0] * 2, [6.0] * 2, [100] * 2)
>>> view = View(graph)
>>> view.show()
drawMarginal2DPDF(firstMarginal, secondMarginal, xMin, xMax, pointNumber, logScaleX=False, logScaleY=False)

Draw the probability density function of a couple of margins.

Parameters:
iint, 1 \leq i \leq n

The index of the first margin of interest.

jint, 1 \leq i \neq j \leq n

The index of the second margin of interest.

x_minlist of 2 floats

The starting values that are used for meshing the x- and y- axes.

x_maxlist of 2 floats, x_{\max} > x_{\min}

The ending values that are used for meshing the x- and y- axes.

n_pointslist of 2 ints

The number of points that are used for meshing the x- and y- axes.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the marginal PDF of the requested couple of margins.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal2DPDF(2, 3, [-6.0] * 2, [6.0] * 2, [100] * 2)
>>> view = View(graph)
>>> view.show()
drawMarginal2DSurvivalFunction(firstMarginal, secondMarginal, xMin, xMax, pointNumber, logScaleX=False, logScaleY=False)

Draw the cumulative distribution function of a couple of margins.

Parameters:
iint, 1 \leq i \leq n

The index of the first margin of interest.

jint, 1 \leq i \neq j \leq n

The index of the second margin of interest.

x_minlist of 2 floats

The starting values that are used for meshing the x- and y- axes.

x_maxlist of 2 floats, x_{\max} > x_{\min}

The ending values that are used for meshing the x- and y- axes.

n_pointslist of 2 ints

The number of points that are used for meshing the x- and y- axes.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the marginal SurvivalFunction of the requested couple of margins.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal(10)
>>> graph = distribution.drawMarginal2DSurvivalFunction(2, 3, [-6.0] * 2, [6.0] * 2, [100] * 2)
>>> view = View(graph)
>>> view.show()
drawPDF(*args)

Draw the graph or of iso-lines of probability density function.

Available constructors:

drawPDF(x_min, x_max, pointNumber, logScale)

drawPDF(lowerCorner, upperCorner, pointNbrInd, logScaleX, logScaleY)

drawPDF(lowerCorner, upperCorner)

Parameters:
x_minfloat, optional

The min-value of the mesh of the x-axis. Defaults uses the quantile associated to the probability level Distribution-QMin from the ResourceMap.

x_maxfloat, optional, x_{\max} > x_{\min}

The max-value of the mesh of the y-axis. Defaults uses the quantile associated to the probability level Distribution-QMax from the ResourceMap.

pointNumberint

The number of points that is used for meshing each axis. Defaults uses DistributionImplementation-DefaultPointNumber from the ResourceMap.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

lowerCornersequence of float, of dimension 2, optional

The lower corner [x_{min}, y_{min}].

upperCornersequence of float, of dimension 2, optional

The upper corner [x_{max}, y_{max}].

pointNbrIndIndices, of dimension 2

Number of points that is used for meshing each axis.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the PDF or its iso_lines.

Notes

Only valid for univariate and bivariate distributions.

Examples

View the PDF of a univariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal()
>>> graph = dist.drawPDF()
>>> graph.setLegends(['normal pdf'])

View the iso-lines PDF of a bivariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal(2)
>>> graph2 = dist.drawPDF()
>>> graph2.setLegends(['iso- normal pdf'])
>>> graph3 = dist.drawPDF([-10, -5],[5, 10], [511, 511])
drawQuantile(*args)

Draw the quantile function.

Parameters:
q_minfloat, in [0,1]

The min value of the mesh of the x-axis.

q_maxfloat, in [0,1]

The max value of the mesh of the x-axis.

n_pointsint, optional

The number of points that is used for meshing the quantile curve. Defaults uses DistributionImplementation-DefaultPointNumber from the ResourceMap.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

Returns:
graphGraph

A graphical representation of the quantile function.

Notes

This is implemented for univariate and bivariate distributions only. In the case of bivariate distributions, defined by its CDF F and its marginals (F_1, F_2), the quantile of order q is the point (F_1(u),F_2(u)) defined by

F(F_1(u), F_2(u)) = q

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> distribution = ot.Normal()
>>> graph = distribution.drawQuantile()
>>> view = View(graph)
>>> view.show()
>>> distribution = ot.ComposedDistribution([ot.Normal(), ot.Exponential(1.0)], ot.ClaytonCopula(0.5))
>>> graph = distribution.drawQuantile()
>>> view = View(graph)
>>> view.show()
drawSurvivalFunction(*args)

Draw the cumulative distribution function.

Available constructors:

drawSurvivalFunction(x_min, x_max, pointNumber, logScale)

drawSurvivalFunction(lowerCorner, upperCorner, pointNbrInd, logScaleX, logScaleY)

drawSurvivalFunction(lowerCorner, upperCorner)

Parameters:
x_minfloat, optional

The min-value of the mesh of the x-axis. Defaults uses the quantile associated to the probability level Distribution-QMin from the ResourceMap.

x_maxfloat, optional, x_{\max} > x_{\min}

The max-value of the mesh of the y-axis. Defaults uses the quantile associated to the probability level Distribution-QMax from the ResourceMap.

pointNumberint

The number of points that is used for meshing each axis. Defaults uses DistributionImplementation-DefaultPointNumber from the ResourceMap.

logScalebool

Flag to tell if the plot is done on a logarithmic scale. Default is False.

lowerCornersequence of float, of dimension 2, optional

The lower corner [x_{min}, y_{min}].

upperCornersequence of float, of dimension 2, optional

The upper corner [x_{max}, y_{max}].

pointNbrIndIndices, of dimension 2

Number of points that is used for meshing each axis.

logScaleXbool

Flag to tell if the plot is done on a logarithmic scale for X. Default is False.

logScaleYbool

Flag to tell if the plot is done on a logarithmic scale for Y. Default is False.

Returns:
graphGraph

A graphical representation of the SurvivalFunction.

Notes

Only valid for univariate and bivariate distributions.

Examples

View the SurvivalFunction of a univariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal()
>>> graph = dist.drawSurvivalFunction()
>>> graph.setLegends(['normal cdf'])

View the iso-lines SurvivalFunction of a bivariate distribution:

>>> import openturns as ot
>>> dist = ot.Normal(2)
>>> graph2 = dist.drawSurvivalFunction()
>>> graph2.setLegends(['iso- normal cdf'])
>>> graph3 = dist.drawSurvivalFunction([-10, -5],[5, 10], [511, 511])
drawUpperExtremalDependenceFunction()

Draw the upper extremal dependence function.

We assume that the distribution is bivariate and that its copula is denoted by C. The upper extremal dependence function u \mapsto \bar{\chi}(u) is defined by:

\bar{\chi}(u) = \frac{2 \log 1-u}{\log \bar{C}(u,u)} - 1, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
graphGraph

Graph of the function u \mapsto \bar{\chi}(u).

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> graph = copula.drawUpperExtremalDependenceFunction()
drawUpperTailDependenceFunction()

Draw the upper tail dependence function.

We assume that the distribution is bivariate and that its copula is denoted by C. The upper tail dependence function u \mapsto \chi(u) is defined by:

\chi(u) = 2 - \frac{\log C(u,u)}{\log u}, \forall u \in [0,1]

Refer to Tail dependence coefficients to get more details.

Returns:
graphGraph

Graph of the function u \mapsto \chi(u).

Examples

>>> import openturns as ot
>>> copula = ot.FrankCopula()
>>> graph = copula.drawUpperTailDependenceFunction()
exp()

Transform distribution by exponential function.

Returns:
distDistribution

The transformed distribution.

getCDFEpsilon()

Accessor to the CDF computation precision.

Returns:
CDFEpsilonfloat

CDF computation precision.

getCentralMoment(n)

Accessor to the componentwise central moments.

Parameters:
kint

The order of the central moment.

Returns:
mPoint

Componentwise central moment of order k.

See also

getMoment

Notes

Central moments are centered with respect to the first-order moment:

\vect{m}^{(k)}_0 = \Tr{\left(\Expect{\left(X_i - \mu_i\right)^k},
                             \quad i = 1, \ldots, n\right)}

getCholesky()

Accessor to the Cholesky factor of the covariance matrix.

Returns:
LSquareMatrix

Cholesky factor of the covariance matrix.

See also

getCovariance
getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getCopula()

Accessor to the copula of the distribution.

Returns:
CDistribution

Copula of the distribution.

getCorrelation()

(ditch me?)

getCovariance()

Accessor to the covariance matrix.

Returns:
SigmaCovarianceMatrix

Covariance matrix.

Notes

The covariance is the second-order central moment. It is defined as:

\mat{\Sigma} & = \Cov{\vect{X}} \\
             & = \Expect{\left(\vect{X} - \vect{\mu}\right)
                         \Tr{\left(\vect{X} - \vect{\mu}\right)}}

getDescription()

Accessor to the componentwise description.

Returns:
descriptionDescription

Description of the components of the distribution.

See also

setDescription
getDimension()

Accessor to the dimension of the distribution.

Returns:
nint

The number of components in the distribution.

getDispersionIndicator()

Dispersion indicator accessor.

Defines a generic metric of the dispersion. When the standard deviation is not defined it falls back to the interquartile. Only available for 1-d distributions.

Returns:
dispersionfloat

Standard deviation or interquartile.

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:
implImplementation

A copy of the underlying implementation object.

getInverseCholesky()

Accessor to the inverse Cholesky factor of the covariance matrix.

Returns:
LinvSquareMatrix

Inverse Cholesky factor of the covariance matrix.

See also

getCholesky
getInverseIsoProbabilisticTransformation()

Accessor to the inverse iso-probabilistic transformation.

Returns:
TinvFunction

Inverse iso-probabilistic transformation.

Notes

The inverse iso-probabilistic transformation is defined as follows:

T^{-1}: \left|\begin{array}{rcl}
            \Rset^n & \rightarrow & \supp{\vect{X}} \\
            \vect{u} & \mapsto & \vect{x}
        \end{array}\right.

getIsoProbabilisticTransformation()

Accessor to the iso-probabilistic transformation.

Refer to Isoprobabilistic transformations.

Returns:
TFunction

Iso-probabilistic transformation.

Notes

The iso-probabilistic transformation is defined as follows:

T: \left|\begin{array}{rcl}
        \supp{\vect{X}} & \rightarrow & \Rset^n \\
        \vect{x} & \mapsto & \vect{u}
   \end{array}\right.

An iso-probabilistic transformation is a diffeomorphism [1] from \supp{\vect{X}} to \Rset^d that maps realizations \vect{x} of a random vector \vect{X} into realizations \vect{y} of another random vector \vect{Y} while preserving probabilities. It is hence defined so that it satisfies:

\begin{eqnarray*}
    \Prob{\bigcap_{i=1}^d X_i \leq x_i}
        & = & \Prob{\bigcap_{i=1}^d Y_i \leq y_i} \\
    F_{\vect{X}}(\vect{x})
        & = & F_{\vect{Y}}(\vect{y})
\end{eqnarray*}

The present implementation of the iso-probabilistic transformation maps realizations \vect{x} into realizations \vect{u} of a random vector \vect{U} with spherical distribution [2]. To be more specific:

  • if the distribution is elliptical, then the transformed distribution is simply made spherical using the Nataf (linear) transformation.

  • if the distribution has an elliptical Copula, then the transformed distribution is made spherical using the generalized Nataf transformation.

  • otherwise, the transformed distribution is the standard multivariate Normal distribution and is obtained by means of the Rosenblatt transformation.

getKendallTau()

Accessor to the Kendall coefficients matrix.

Returns:
tau: SquareMatrix

Kendall coefficients matrix.

Notes

The Kendall coefficients matrix is defined as:

\mat{\tau} = \Big[& \Prob{X_i < x_i \cap X_j < x_j
                          \cup
                          X_i > x_i \cap X_j > x_j} \\
                  & - \Prob{X_i < x_i \cap X_j > x_j
                            \cup
                            X_i > x_i \cap X_j < x_j},
                  \quad i,j = 1, \ldots, n\Big]

getKurtosis()

Accessor to the componentwise kurtosis.

Returns:
kPoint

Componentwise kurtosis.

Notes

The kurtosis is the fourth-order central moment standardized by the standard deviation:

\vect{\kappa} = \Tr{\left(\Expect{\left(\frac{X_i - \mu_i}
                                             {\sigma_i}\right)^4},
                          \quad i = 1, \ldots, n\right)}

getMarginal(*args)

Accessor to marginal distributions.

Parameters:
iint or list of ints, 1 \leq i \leq n

Component(s) indice(s).

Returns:
distributionDistribution

The marginal distribution of the selected component(s).

getMean()

Accessor to the mean.

Returns:
kPoint

Mean.

Notes

The mean is the first-order moment:

\vect{\mu} = \Tr{\left(\Expect{X_i}, \quad i = 1, \ldots, n\right)}

getMoment(n)

Accessor to the componentwise moments.

Parameters:
kint

The order of the moment.

Returns:
mPoint

Componentwise moment of order k.

Notes

The componentwise moment of order k is defined as:

\vect{m}^{(k)} = \Tr{\left(\Expect{X_i^k}, \quad i = 1, \ldots, n\right)}

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getPDFEpsilon()

Accessor to the PDF computation precision.

Returns:
PDFEpsilonfloat

PDF computation precision.

getParameter()

Accessor to the parameter of the distribution.

Returns:
parameterPoint

Parameter values.

getParameterDescription()

Accessor to the parameter description of the distribution.

Returns:
descriptionDescription

Parameter names.

getParameterDimension()

Accessor to the number of parameters in the distribution.

Returns:
n_parametersint

Number of parameters in the distribution.

getParametersCollection()

Accessor to the parameter of the distribution.

Returns:
parametersPointWithDescription

Dictionary-like object with parameters names and values.

getPearsonCorrelation()

Accessor to the Pearson correlation matrix.

Returns:
RCorrelationMatrix

Pearson’s correlation matrix.

See also

getCovariance

Notes

Pearson’s correlation is defined as the normalized covariance matrix:

\mat{\rho} & = \left[\frac{\Cov{X_i, X_j}}{\sqrt{\Var{X_i}\Var{X_j}}},
                     \quad i,j = 1, \ldots, n\right] \\
           & = \left[\frac{\Sigma_{i,j}}{\sqrt{\Sigma_{i,i}\Sigma_{j,j}}},
                     \quad i,j = 1, \ldots, n\right]

getPositionIndicator()

Position indicator accessor.

Defines a generic metric of the position. When the mean is not defined it falls back to the median. Available only for 1-d distributions.

Returns:
positionfloat

Mean or median of the distribution.

getProbabilities()

Accessor to the discrete probability levels.

Returns:
probabilitiesPoint

The probability levels of a discrete distribution.

getRange()

Accessor to the range of the distribution.

Returns:
rangeInterval

Range of the distribution.

See also

getSupport

Notes

The mathematical range is the smallest closed interval outside of which the PDF is zero. The numerical range is the interval outside of which the PDF is rounded to zero in double precision.

getRealization()

Accessor to a pseudo-random realization from the distribution.

Refer to Distribution realizations.

Returns:
pointPoint

A pseudo-random realization of the distribution.

getRoughness()

Accessor to roughness of the distribution.

Returns:
rfloat

Roughness of the distribution.

See also

computePDF

Notes

The roughness of the distribution is defined as the \cL^2-norm of its PDF:

r = \int_{\supp{\vect{X}}} f_{\vect{X}}(\vect{x})^2 \di{\vect{x}}

getSample(size)

Accessor to a pseudo-random sample from the distribution.

Parameters:
sizeint

Sample size.

Returns:
sampleSample

A pseudo-random sample of the distribution.

getSampleByInversion(size)

Accessor to a pseudo-random sample from the distribution.

Parameters:
sizeint

Sample size.

Returns:
sampleSample

A pseudo-random sample of the distribution based on conditional quantiles.

getSampleByQMC(size)

Accessor to a low discrepancy sample from the distribution.

Parameters:
sizeint

Sample size.

Returns:
sampleSample

A low discrepancy sample of the distribution based on Sobol’s sequences and conditional quantiles.

getShapeMatrix()

Accessor to the shape matrix of the underlying copula if it is elliptical.

Returns:
shapeCorrelationMatrix

Shape matrix of the elliptical copula of a distribution.

Notes

This is not the Pearson correlation matrix.

getShiftedMoment(n, shift)

Accessor to the componentwise shifted moments.

Parameters:
kint

The order of the shifted moment.

shiftsequence of float

The shift of the moment.

Returns:
mPoint

Componentwise central moment of order k.

Notes

The moments are centered with respect to the given shift \vect{s}:

\vect{m}^{(k)}_0 = \Tr{\left(\Expect{\left(X_i - s_i\right)^k},
                             \quad i = 1, \ldots, n\right)}

getSingularities()

Accessor to the singularities of the PDF function.

It is defined for univariate distributions only, and gives all the singularities (ie discontinuities of any order) strictly inside of the range of the distribution.

Returns:
singularitiesPoint

The singularities of the PDF of an univariate distribution.

getSkewness()

Accessor to the componentwise skewness.

Returns:
dPoint

Componentwise skewness.

Notes

The skewness is the third-order central moment standardized by the standard deviation:

\vect{\delta} = \Tr{\left(\Expect{\left(\frac{X_i - \mu_i}
                                             {\sigma_i}\right)^3},
                          \quad i = 1, \ldots, n\right)}

getSpearmanCorrelation()

Accessor to the Spearman correlation matrix.

Returns:
RCorrelationMatrix

Spearman’s correlation matrix.

See also

getKendallTau

Notes

Spearman’s (rank) correlation is defined as the normalized covariance matrix of the copula (ie that of the uniform margins):

\mat{\rho_S} = \left[\frac{\Cov{F_{X_i}(X_i), F_{X_j}(X_j)}}
                          {\sqrt{\Var{F_{X_i}(X_i)} \Var{F_{X_j}(X_j)}}},
                     \quad i,j = 1, \ldots, n\right]

getStandardDeviation()

Accessor to the componentwise standard deviation.

The standard deviation is the square root of the variance.

Returns:
sigmaPoint

Componentwise standard deviation.

See also

getCovariance
getStandardDistribution()

Accessor to the standard distribution.

Returns:
standard_distributionDistribution

Standard distribution.

Notes

The standard distribution is determined according to the distribution properties. This is the target distribution achieved by the iso-probabilistic transformation.

getStandardRepresentative()

Accessor to the standard representative distribution in the parametric family.

Returns:
std_repr_distDistribution

Standard representative distribution.

Notes

The standard representative distribution is defined on a distribution by distribution basis, most of the time by scaling the distribution with bounded support to [0,1] or by standardizing (ie zero mean, unit variance) the distributions with unbounded support. It is the member of the family for which orthonormal polynomials will be built using generic algorithms of orthonormalization.

getSupport(*args)

Accessor to the support of the distribution.

Parameters:
intervalInterval

An interval to intersect with the support of the discrete part of the distribution.

Returns:
supportInterval

The intersection of the support of the discrete part of the distribution with the given interval.

See also

getRange

Notes

The mathematical support \supp{\vect{X}} of the discrete part of a distribution is the collection of points with nonzero probability.

This is yet implemented for discrete distributions only.

hasEllipticalCopula()

Test whether the copula of the distribution is elliptical or not.

Returns:
testbool

Answer.

See also

isElliptical
hasIndependentCopula()

Test whether the copula of the distribution is the independent one.

Returns:
testbool

Answer.

inverse()

Transform distribution by inverse function.

Returns:
distDistribution

The transformed distribution.

isContinuous()

Test whether the distribution is continuous or not.

Returns:
testbool

Answer.

isCopula()

Test whether the distribution is a copula or not.

Returns:
testbool

Answer.

Notes

A copula is a distribution with uniform margins on [0; 1].

isDiscrete()

Test whether the distribution is discrete or not.

Returns:
testbool

Answer.

isElliptical()

Test whether the distribution is elliptical or not.

Returns:
testbool

Answer.

Notes

A multivariate distribution is said to be elliptical if its characteristic function is of the form:

\phi(\vect{t}) = \exp\left(i \Tr{\vect{t}} \vect{\mu}\right)
                 \Psi\left(\Tr{\vect{t}} \mat{\Sigma} \vect{t}\right),
                 \quad \vect{t} \in \Rset^n

for specified vector \vect{\mu} and positive-definite matrix \mat{\Sigma}. The function \Psi is known as the characteristic generator of the elliptical distribution.

isIntegral()

Test whether the distribution is integer-valued or not.

Returns:
testbool

Answer.

ln()

Transform distribution by natural logarithm function.

Returns:
distDistribution

The transformed distribution.

log()

Transform distribution by natural logarithm function.

Returns:
distDistribution

The transformed distribution.

setDescription(description)

Accessor to the componentwise description.

Parameters:
descriptionsequence of str

Description of the components of the distribution.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setParameter(parameters)

Accessor to the parameter of the distribution.

Parameters:
parametersequence of float

Parameter values.

setParametersCollection(*args)

Accessor to the parameter of the distribution.

Parameters:
parametersPointWithDescription

Dictionary-like object with parameters names and values.

sin()

Transform distribution by sine function.

Returns:
distDistribution

The transformed distribution.

sinh()

Transform distribution by sinh function.

Returns:
distDistribution

The transformed distribution.

sqr()

Transform distribution by square function.

Returns:
distDistribution

The transformed distribution.

sqrt()

Transform distribution by square root function.

Returns:
distDistribution

The transformed distribution.

tan()

Transform distribution by tangent function.

Returns:
distDistribution

The transformed distribution.

tanh()

Transform distribution by tanh function.

Returns:
distDistribution

The transformed distribution.

Examples using the class

Fit a distribution by maximum likelihood

Fit a distribution by maximum likelihood

Fit a parametric distribution

Fit a parametric distribution

Model a singular multivariate distribution

Model a singular multivariate distribution

Define a distribution from quantiles

Define a distribution from quantiles

Get the asymptotic distribution of the estimators

Get the asymptotic distribution of the estimators

Estimate a GEV on the Venice sea-levels data

Estimate a GEV on the Venice sea-levels data

Bandwidth sensitivity in kernel smoothing

Bandwidth sensitivity in kernel smoothing

Estimate a conditional quantile

Estimate a conditional quantile

Estimate a multivariate distribution

Estimate a multivariate distribution

Fit a non parametric distribution

Fit a non parametric distribution

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on race times data

Estimate a GEV on race times data

Test a discrete distribution

Test a discrete distribution

Draw the QQ-Plot

Draw the QQ-Plot

Use the Kolmogorov/Lilliefors test

Use the Kolmogorov/Lilliefors test

Select fitted distributions

Select fitted distributions

Test the copula

Test the copula

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : understand the statistics

Fit a parametric copula

Fit a parametric copula

Fit a non parametric copula

Fit a non parametric copula

Truncate a distribution

Truncate a distribution

Transform a distribution

Transform a distribution

Overview of univariate distribution management

Overview of univariate distribution management

Distribution manipulation

Distribution manipulation

Create a customized distribution or copula

Create a customized distribution or copula

Extract the copula from a distribution

Extract the copula from a distribution

Aggregate processes

Aggregate processes

Use the Box-Cox transformation

Use the Box-Cox transformation

Fit a distribution from an input sample

Fit a distribution from an input sample

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a polynomial chaos metamodel by integration on the cantilever beam

Advanced polynomial chaos construction

Advanced polynomial chaos construction

Create a sparse chaos by integration

Create a sparse chaos by integration

Evaluate the mean of a random vector by simulations

Evaluate the mean of a random vector by simulations

Specify a simulation algorithm

Specify a simulation algorithm

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

Cross Entropy Importance Sampling

Cross Entropy Importance Sampling

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

Create univariate functions

Create univariate functions

Create multivariate functions

Create multivariate functions

Calibration of the logistic model

Calibration of the logistic model

Calibration of the deflection of a tube

Calibration of the deflection of a tube

Calibration of the flooding model

Calibration of the flooding model

Calibration of the Chaboche mechanical model

Calibration of the Chaboche mechanical model

Gibbs sampling of the posterior distribution

Gibbs sampling of the posterior distribution

Bayesian calibration of a computer code

Bayesian calibration of a computer code

Sampling from an unnormalized probability density

Sampling from an unnormalized probability density

Bayesian calibration of the flooding model

Bayesian calibration of the flooding model

Posterior sampling using a PythonDistribution

Posterior sampling using a PythonDistribution

Mix/max search and sensitivity from design

Mix/max search and sensitivity from design

Mix/max search using optimization

Mix/max search using optimization