Multinomial distribution¶

(Source code, png, hires.png, pdf)

../../_images/openturns-Multinomial-1.png

class Multinomial(*args)¶

Multinomial distribution.

Available constructors:: Multinomial(N=1, P=[0.5])

Parameters

Nint: Number of trials.
Psequence of float, $p_i \geq 0, i = 1, \ldots, d$ and $\sum_{i = 1}^d p_i \leq 1$: Component probabilities.

See also

Dirichlet

Notes

This distribution differs from the classical multinomial distribution definition. The classical multinomial distribution is constrained by $\sum_{i = 1}^d p_i=1$ and is supported by $\left\{\vect{x} \in \Nset^d\, |\, \sum_{i = 1}^d x_i = N\right\}$ . The OpenTURNS distribution is constrained by $q=\sum_{i = 1}^d p_i \ leq 1$ and is supported in general by $\cS=\left\{\vect{x} \in \Nset^d\, |\, \sum_{i = 1}^d x_i \leq N\right\}$ .

Its probability density function is defined as:

$\Prob{\vect{X} = \vect{x}} = \frac{N!}{x_1! \ldots x_d! (N - s)!} p_1^{x_1} \ldots p_d^{x_d} (1 - q)^{N - s}\mathbf{1}_{\vect{x} \in \cS}$

where $s=\sum_{i = 1}^d x_i$ .

If $q=1$ , then the distribution generates realizations $\vect{x}=(x_1,\dots,x_d)$ such that $\sum_{i = 1}^d x_i = N$ . In this case, we recover the classical definition of the multinomial distribution.

If $q<1$ , then the distribution generates realizations $\vect{x}=(x_1,\dots,x_d)$ such that $\sum_{i = 1}^d x_i \leq N$ . It allows to recover the binomial distribution as a special case of multinomial distribution when $d=1$ .

Its first moments are:

$\begin{eqnarray*} \Expect{X_i} & = & N p_i, \quad i = 1, \ldots, n \\ \Var{X_i} & = & N p_i (1 - p_i), \quad i = 1, \ldots, n \\ \Cov{X_i, X_j} & = & - N p_i p_j, \quad i, j = 1, \ldots, n, i \neq j \end{eqnarray*}$

Examples

Create a distribution:

>>> import openturns as ot
>>> distribution = ot.Multinomial(1, [0.5])

Draw a sample:

>>> sample = distribution.getSample(5)

Methods

`abs`(self)	Transform distribution by absolute value function.
`acos`(self)	Transform distribution by arccosine function.
`acosh`(self)	Transform distribution by acosh function.
`asin`(self)	Transform distribution by arcsine function.
`asinh`(self)	Transform distribution by asinh function.
`atan`(self)	Transform distribution by arctangent function.
`atanh`(self)	Transform distribution by atanh function.
`cbrt`(self)	Transform distribution by cubic root function.
`computeBilateralConfidenceInterval`(self, prob)	Compute a bilateral confidence interval.
`computeBilateralConfidenceIntervalWithMarginalProbability`(…)	Compute a bilateral confidence interval.
`computeCDF`(self, \*args)	Compute the cumulative distribution function.
`computeCDFGradient`(self, \*args)	Compute the gradient of the cumulative distribution function.
`computeCharacteristicFunction`(self, \*args)	Compute the characteristic function.
`computeComplementaryCDF`(self, \*args)	Compute the complementary cumulative distribution function.
`computeConditionalCDF`(self, \*args)	Compute the conditional cumulative distribution function.
`computeConditionalDDF`(self, x, y)	Compute the conditional derivative density function of the last component.
`computeConditionalPDF`(self, \*args)	Compute the conditional probability density function.
`computeConditionalQuantile`(self, \*args)	Compute the conditional quantile function of the last component.
`computeDDF`(self, \*args)	Compute the derivative density function.
`computeDensityGenerator`(self, betaSquare)	Compute the probability density function of the characteristic generator.
`computeDensityGeneratorDerivative`(self, …)	Compute the first-order derivative of the probability density function.
`computeDensityGeneratorSecondDerivative`(…)	Compute the second-order derivative of the probability density function.
`computeEntropy`(self)	Compute the entropy of the distribution.
`computeGeneratingFunction`(self, \*args)	Compute the probability-generating function.
`computeInverseSurvivalFunction`(self, point)	Compute the inverse survival function.
`computeLogCharacteristicFunction`(self, \*args)	Compute the logarithm of the characteristic function.
`computeLogGeneratingFunction`(self, \*args)	Compute the logarithm of the probability-generating function.
`computeLogPDF`(self, \*args)	Compute the logarithm of the probability density function.
`computeLogPDFGradient`(self, \*args)	Compute the gradient of the log probability density function.
`computeMinimumVolumeInterval`(self, prob)	Compute the confidence interval with minimum volume.
`computeMinimumVolumeIntervalWithMarginalProbability`(…)	Compute the confidence interval with minimum volume.
`computeMinimumVolumeLevelSet`(self, prob)	Compute the confidence domain with minimum volume.
`computeMinimumVolumeLevelSetWithThreshold`(…)	Compute the confidence domain with minimum volume.
`computePDF`(self, \*args)	Compute the probability density function.
`computePDFGradient`(self, \*args)	Compute the gradient of the probability density function.
`computeProbability`(self, interval)	Compute the interval probability.
`computeQuantile`(self, \*args)	Compute the quantile function.
`computeRadialDistributionCDF`(self, radius[, …])	Compute the cumulative distribution function of the squared radius.
`computeScalarQuantile`(self, prob[, tail])	Compute the quantile function for univariate distributions.
`computeSequentialConditionalCDF`(self, x)	Compute the sequential conditional cumulative distribution functions.
`computeSequentialConditionalDDF`(self, x)	Compute the sequential conditional derivative density function.
`computeSequentialConditionalPDF`(self, x)	Compute the sequential conditional probability density function.
`computeSequentialConditionalQuantile`(self, q)	Compute the conditional quantile function of the last component.
`computeSurvivalFunction`(self, \*args)	Compute the survival function.
`computeUnilateralConfidenceInterval`(self, prob)	Compute a unilateral confidence interval.
`computeUnilateralConfidenceIntervalWithMarginalProbability`(…)	Compute a unilateral confidence interval.
`cos`(self)	Transform distribution by cosine function.
`cosh`(self)	Transform distribution by cosh function.
`drawCDF`(self, \*args)	Draw the cumulative distribution function.
`drawLogPDF`(self, \*args)	Draw the graph or of iso-lines of log-probability density function.
`drawMarginal1DCDF`(self, marginalIndex, xMin, …)	Draw the cumulative distribution function of a margin.
`drawMarginal1DLogPDF`(self, marginalIndex, …)	Draw the log-probability density function of a margin.
`drawMarginal1DPDF`(self, marginalIndex, xMin, …)	Draw the probability density function of a margin.
`drawMarginal1DSurvivalFunction`(self, …[, …])	Draw the cumulative distribution function of a margin.
`drawMarginal2DCDF`(self, firstMarginal, …)	Draw the cumulative distribution function of a couple of margins.
`drawMarginal2DLogPDF`(self, firstMarginal, …)	Draw the log-probability density function of a couple of margins.
`drawMarginal2DPDF`(self, firstMarginal, …)	Draw the probability density function of a couple of margins.
`drawMarginal2DSurvivalFunction`(self, …[, …])	Draw the cumulative distribution function of a couple of margins.
`drawPDF`(self, \*args)	Draw the graph or of iso-lines of probability density function.
`drawQuantile`(self, \*args)	Draw the quantile function.
`drawSurvivalFunction`(self, \*args)	Draw the cumulative distribution function.
`exp`(self)	Transform distribution by exponential function.
`getCDFEpsilon`(self)	Accessor to the CDF computation precision.
`getCenteredMoment`(self, n)	Accessor to the componentwise centered moments.
`getCholesky`(self)	Accessor to the Cholesky factor of the covariance matrix.
`getClassName`(self)	Accessor to the object’s name.
`getCopula`(self)	Accessor to the copula of the distribution.
`getCorrelation`(self)	(ditch me?)
`getCovariance`(self)	Accessor to the covariance matrix.
`getDescription`(self)	Accessor to the componentwise description.
`getDimension`(self)	Accessor to the dimension of the distribution.
`getDispersionIndicator`(self)	Dispersion indicator accessor.
`getId`(self)	Accessor to the object’s id.
`getIntegrationNodesNumber`(self)	Accessor to the number of Gauss integration points.
`getInverseCholesky`(self)	Accessor to the inverse Cholesky factor of the covariance matrix.
`getInverseIsoProbabilisticTransformation`(self)	Accessor to the inverse iso-probabilistic transformation.
`getIsoProbabilisticTransformation`(self)	Accessor to the iso-probabilistic transformation.
`getKendallTau`(self)	Accessor to the Kendall coefficients matrix.
`getKurtosis`(self)	Accessor to the componentwise kurtosis.
`getLinearCorrelation`(self)	(ditch me?)
`getMarginal`(self, \*args)	Accessor to marginal distributions.
`getMean`(self)	Accessor to the mean.
`getMoment`(self, n)	Accessor to the componentwise moments.
`getN`(self)	Accessor to the number of experiments parameter $N$ .
`getName`(self)	Accessor to the object’s name.
`getP`(self)	Accessor to the component probabilities parameter $\vect{p}$ .
`getPDFEpsilon`(self)	Accessor to the PDF computation precision.
`getParameter`(self)	Accessor to the parameter of the distribution.
`getParameterDescription`(self)	Accessor to the parameter description of the distribution.
`getParameterDimension`(self)	Accessor to the number of parameters in the distribution.
`getParametersCollection`(self)	Accessor to the parameter of the distribution.
`getPearsonCorrelation`(self)	Accessor to the Pearson correlation matrix.
`getPositionIndicator`(self)	Position indicator accessor.
`getProbabilities`(self)	Accessor to the discrete probability levels.
`getRange`(self)	Accessor to the range of the distribution.
`getRealization`(self)	Accessor to a pseudo-random realization from the distribution.
`getRoughness`(self)	Accessor to roughness of the distribution.
`getSample`(self, size)	Accessor to a pseudo-random sample from the distribution.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getShapeMatrix`(self)	Accessor to the shape matrix of the underlying copula if it is elliptical.
`getShiftedMoment`(self, n, shift)	Accessor to the componentwise shifted moments.
`getSingularities`(self)	Accessor to the singularities of the PDF function.
`getSkewness`(self)	Accessor to the componentwise skewness.
`getSpearmanCorrelation`(self)	Accessor to the Spearman correlation matrix.
`getStandardDeviation`(self)	Accessor to the componentwise standard deviation.
`getStandardDistribution`(self)	Accessor to the standard distribution.
`getStandardMoment`(self, n)	Accessor to the componentwise standard moments.
`getStandardRepresentative`(self)	Accessor to the standard representative distribution in the parametric family.
`getSupport`(self, \*args)	Accessor to the support of the distribution.
`getVisibility`(self)	Accessor to the object’s visibility state.
`hasEllipticalCopula`(self)	Test whether the copula of the distribution is elliptical or not.
`hasIndependentCopula`(self)	Test whether the copula of the distribution is the independent one.
`hasName`(self)	Test if the object is named.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`inverse`(self)	Transform distribution by inverse function.
`isContinuous`(self)	Test whether the distribution is continuous or not.
`isCopula`(self)	Test whether the distribution is a copula or not.
`isDiscrete`(self)	Test whether the distribution is discrete or not.
`isElliptical`(self)	Test whether the distribution is elliptical or not.
`isIntegral`(self)	Test whether the distribution is integer-valued or not.
`ln`(self)	Transform distribution by natural logarithm function.
`log`(self)	Transform distribution by natural logarithm function.
`setDescription`(self, description)	Accessor to the componentwise description.
`setIntegrationNodesNumber`(self, …)	Accessor to the number of Gauss integration points.
`setN`(self, n)	Accessor to the number of experiments parameter $N$ .
`setName`(self, name)	Accessor to the object’s name.
`setP`(self, p)	Accessor to the component probabilities parameter $\vect{p}$ .
`setParameter`(self, parameter)	Accessor to the parameter of the distribution.
`setParametersCollection`(self, \*args)	Accessor to the parameter of the distribution.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.
`sin`(self)	Transform distribution by sine function.
`sinh`(self)	Transform distribution by sinh function.
`sqr`(self)	Transform distribution by square function.
`sqrt`(self)	Transform distribution by square root function.
`tan`(self)	Transform distribution by tangent function.
`tanh`(self)	Transform distribution by tanh function.

getEta
getSmallA
getSupportEpsilon
setEta
setSmallA
setSupportEpsilon

__init__(self, \*args)¶: Initialize self. See help(type(self)) for accurate signature.

abs(self)¶

Transform distribution by absolute value function.

Returns

distDistribution: The transformed distribution.

acos(self)¶

Transform distribution by arccosine function.

Returns

distDistribution: The transformed distribution.

acosh(self)¶

Transform distribution by acosh function.

Returns

distDistribution: The transformed distribution.

asin(self)¶

Transform distribution by arcsine function.

Returns

distDistribution: The transformed distribution.

asinh(self)¶

Transform distribution by asinh function.

Returns

distDistribution: The transformed distribution.

atan(self)¶

Transform distribution by arctangent function.

Returns

distDistribution: The transformed distribution.

atanh(self)¶

Transform distribution by atanh function.

Returns

distDistribution: The transformed distribution.

cbrt(self)¶

Transform distribution by cubic root function.

Returns

distDistribution: The transformed distribution.

computeBilateralConfidenceInterval(self, 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(self, 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(self, \*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(self, \*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(self, \*args)¶

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(self, \*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) $X$ .

See also

computeSurvivalFunction

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(self, \*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(self, 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 $X_n$ , $X_{cond}$ .

See also

computeDDF, computeConditionalCDF

computeConditionalPDF(self, \*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 $X_n$ , $X_{cond}$ .

See also

computePDF, computeConditionalCDF

computeConditionalQuantile(self, \*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 $p$ , $X_{cond}$ .

See also

computeQuantile, computeConditionalCDF

computeDDF(self, \*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(self, 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 $X$ .

See also

isElliptical, computePDF

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(self, 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 $X$ .

See also

isElliptical, computeDensityGenerator

Notes

This function only exists for elliptical distributions.

computeDensityGeneratorSecondDerivative(self, 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 $X$ .

See also

isElliptical, computeDensityGenerator

Notes

This function only exists for elliptical distributions.

computeEntropy(self)¶

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(self, \*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(self, point)¶

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.

See also

computeQuantile, computeSurvivalFunction

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(self, \*args)¶

Compute the logarithm of the characteristic function.

Parameters

tfloat: Characteristic function input.

Returns

phicomplex: Logarithm of the characteristic function value at input $t$ .

See also

computeCharacteristicFunction

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(self, \*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 $X$ .

See also

isDiscrete, computeGeneratingFunction

Notes

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

computeLogPDF(self, \*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(self, \*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 $X$ .

computeMinimumVolumeInterval(self, 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)

computeMinimumVolumeIntervalWithMarginalProbability(self, 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)

computeMinimumVolumeLevelSet(self, 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)

computeMinimumVolumeLevelSetWithThreshold(self, 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(self, \*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(self, \*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(self, 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(self, \*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(self, 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(self, 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(self, 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(self, 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(self, 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(self, 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(self, \*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.

See also

computeComplementaryCDF

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(self, 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(self, 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)

cos(self)¶

Transform distribution by cosine function.

Returns

distDistribution: The transformed distribution.

cosh(self)¶

Transform distribution by cosh function.

Returns

distDistribution: The transformed distribution.

drawCDF(self, \*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.

See also

computeCDF, viewer.View, ResourceMap

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(self, \*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.

See also

computeLogPDF, viewer.View, ResourceMap

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])

drawMarginal1DCDF(self, 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.

See also

computeCDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computeLogPDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computePDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computeSurvivalFunction, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computeCDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computeLogPDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computePDF, getMarginal, viewer.View, ResourceMap

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(self, 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.

See also

computeSurvivalFunction, getMarginal, viewer.View, ResourceMap

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(self, \*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.

See also

computePDF, viewer.View, ResourceMap

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(self, \*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.

See also

computeQuantile, viewer.View, ResourceMap

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(self, \*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.

See also

computeSurvivalFunction, viewer.View, ResourceMap

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])

exp(self)¶

Transform distribution by exponential function.

Returns

distDistribution: The transformed distribution.

getCDFEpsilon(self)¶

Accessor to the CDF computation precision.

Returns

CDFEpsilonfloat: CDF computation precision.

getCenteredMoment(self, n)¶

Accessor to the componentwise centered moments.

Parameters

kint: The order of the centered moment.

Returns

mPoint: Componentwise centered moment of order $k$ .

See also

getMoment

Notes

Centered 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(self)¶

Accessor to the Cholesky factor of the covariance matrix.

Returns

LSquareMatrix: Cholesky factor of the covariance matrix.

See also

getCovariance

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getCopula(self)¶

Accessor to the copula of the distribution.

Returns

CDistribution: Copula of the distribution.

See also

ComposedDistribution

getCorrelation(self)¶: (ditch me?)

getCovariance(self)¶

Accessor to the covariance matrix.

Returns

SigmaCovarianceMatrix: Covariance matrix.

Notes

The covariance is the second-order centered 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(self)¶

Accessor to the componentwise description.

Returns

descriptionDescription: Description of the components of the distribution.

See also

setDescription

getDimension(self)¶

Accessor to the dimension of the distribution.

Returns

nint: The number of components in the distribution.

getDispersionIndicator(self)¶

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(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getIntegrationNodesNumber(self)¶

Accessor to the number of Gauss integration points.

Returns

Nint: Number of integration points.

getInverseCholesky(self)¶

Accessor to the inverse Cholesky factor of the covariance matrix.

Returns

LinvSquareMatrix: Inverse Cholesky factor of the covariance matrix.

See also

getCholesky

getInverseIsoProbabilisticTransformation(self)¶

Accessor to the inverse iso-probabilistic transformation.

Returns

TinvFunction: Inverse iso-probabilistic transformation.

See also

getIsoProbabilisticTransformation

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(self)¶

Accessor to the iso-probabilistic transformation.

Refer to Isoprobabilistic transformations.

Returns

TFunction: Iso-probabilistic transformation.

See also

getInverseIsoProbabilisticTransformation, isElliptical, hasEllipticalCopula

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.

1: A differentiable map $f$ is called a diffeomorphism if it is a bijection and its inverse $f^{-1}$ is differentiable as well. Hence, the iso-probabilistic transformation implements a gradient (and even a Hessian).
2: A distribution is said to be spherical if it is invariant by rotation. Mathematically, $\vect{U}$ has a spherical distribution if:

$\mat{Q}\,\vect{U} \sim \vect{U}, \quad \forall \mat{Q} \in \cO_n(\Rset)$

getKendallTau(self)¶

Accessor to the Kendall coefficients matrix.

Returns

tau: SquareMatrix: Kendall coefficients matrix.

See also

getSpearmanCorrelation

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(self)¶

Accessor to the componentwise kurtosis.

Returns

kPoint: Componentwise kurtosis.

Notes

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

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

getLinearCorrelation(self)¶: (ditch me?)

getMarginal(self, \*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(self)¶

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(self, 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:

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

getN(self)¶

Accessor to the number of experiments parameter $N$ .

Returns

Nint: Number of experiments $N$ .

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getP(self)¶

Accessor to the component probabilities parameter $\vect{p}$ .

Returns

PPoint: Component probabilities $\vect{p}$ .

getPDFEpsilon(self)¶

Accessor to the PDF computation precision.

Returns

PDFEpsilonfloat: PDF computation precision.

getParameter(self)¶

Accessor to the parameter of the distribution.

Returns

parameterPoint: Parameter values.

getParameterDescription(self)¶

Accessor to the parameter description of the distribution.

Returns

descriptionDescription: Parameter names.

getParameterDimension(self)¶

Accessor to the number of parameters in the distribution.

Returns

n_parametersint: Number of parameters in the distribution.

See also

getParametersCollection

getParametersCollection(self)¶

Accessor to the parameter of the distribution.

Returns

parametersPointWithDescription: Dictionary-like object with parameters names and values.

getPearsonCorrelation(self)¶

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(self)¶

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(self)¶

Accessor to the discrete probability levels.

Returns

probabilitiesPoint: The probability levels of a discrete distribution.

getRange(self)¶

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(self)¶

Accessor to a pseudo-random realization from the distribution.

Refer to Distribution realizations.

Returns

pointPoint: A pseudo-random realization of the distribution.

See also

getSample, RandomGenerator

getRoughness(self)¶

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(self, size)¶

Accessor to a pseudo-random sample from the distribution.

Parameters

sizeint: Sample size.

Returns

sampleSample: A pseudo-random sample of the distribution.

See also

getRealization, RandomGenerator

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getShapeMatrix(self)¶

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

Returns

shapeCorrelationMatrix: Shape matrix of the elliptical copula of a distribution.

See also

getPearsonCorrelation

Notes

This is not the Pearson correlation matrix.

getShiftedMoment(self, 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 centered moment of order $k$ .

See also

getMoment, getCenteredMoment

Notes

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

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

getSingularities(self)¶

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(self)¶

Accessor to the componentwise skewness.

Returns

dPoint: Componentwise skewness.

Notes

The skewness is the third-order centered 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(self)¶

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(self)¶

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(self)¶

Accessor to the standard distribution.

Returns

standard_distributionDistribution: Standard distribution.

See also

getIsoProbabilisticTransformation

Notes

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

getStandardMoment(self, n)¶

Accessor to the componentwise standard moments.

Parameters

kint: The order of the standard moment.

Returns

mPoint: Componentwise standard moment of order $k$ .

See also

getStandardRepresentative

Notes

Standard moments are the raw moments of the standard representative of the parametric family of distributions.

getStandardRepresentative(self)¶

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(self, \*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.

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasEllipticalCopula(self)¶

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

Returns

testbool: Answer.

See also

isElliptical

hasIndependentCopula(self)¶

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

Returns

testbool: Answer.

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool: True if the name is not empty and not the default one.

inverse(self)¶

Transform distribution by inverse function.

Returns

distDistribution: The transformed distribution.

isContinuous(self)¶

Test whether the distribution is continuous or not.

Returns

testbool: Answer.

isCopula(self)¶

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(self)¶

Test whether the distribution is discrete or not.

Returns

testbool: Answer.

isElliptical(self)¶

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(self)¶

Test whether the distribution is integer-valued or not.

Returns

testbool: Answer.

ln(self)¶

Transform distribution by natural logarithm function.

Returns

distDistribution: The transformed distribution.

log(self)¶

Transform distribution by natural logarithm function.

Returns

distDistribution: The transformed distribution.

setDescription(self, description)¶

Accessor to the componentwise description.

Parameters

descriptionsequence of str: Description of the components of the distribution.

setIntegrationNodesNumber(self, integrationNodesNumber)¶

Accessor to the number of Gauss integration points.

Parameters

Nint: Number of integration points.

setN(self, n)¶

Accessor to the number of experiments parameter $N$ .

Parameters

Nint, $\sum_{i = 1}^n x_i \leq N$: Number of experiments $N$ .

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setP(self, p)¶

Accessor to the component probabilities parameter $\vect{p}$ .

Parameters

Psequence of float, $0 \leq p_i, i = 1, \ldots, n$ and $\sum_{i = 1}^n p_i \leq 1$: Component probabilities (all positive with sum less than unity).

setParameter(self, parameter)¶

Accessor to the parameter of the distribution.

Parameters

parametersequence of float: Parameter values.

setParametersCollection(self, \*args)¶

Accessor to the parameter of the distribution.

Parameters

parametersPointWithDescription: Dictionary-like object with parameters names and values.

setShadowedId(self, id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setVisibility(self, visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

sin(self)¶

Transform distribution by sine function.

Returns

distDistribution: The transformed distribution.

sinh(self)¶

Transform distribution by sinh function.

Returns

distDistribution: The transformed distribution.

sqr(self)¶

Transform distribution by square function.

Returns

distDistribution: The transformed distribution.

sqrt(self)¶

Transform distribution by square root function.

Returns

distDistribution: The transformed distribution.

tan(self)¶

Transform distribution by tangent function.

Returns

distDistribution: The transformed distribution.

tanh(self)¶

Transform distribution by tanh function.

Returns

distDistribution: The transformed distribution.

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