# Estimate a distribution by maximum likelihood¶

In this example we are going to estimate the parameters of a parametric by generic numerical optimization of the likelihood.

In [1]:

from __future__ import print_function
import openturns as ot
import math as m

In [2]:

# Create data from a gaussian pdf with mu=4, sigma=1.5
sample = ot.Normal(4.0, 1.5).getSample(200)

In [3]:

# Create the search interval of (mu, sigma): the constraint is sigma>0
lowerBound = [-1.0, 1.0e-4]
upperBound = [-1.0, -1.0]
finiteLowerBound = [False, True]
finiteUpperBound = [False, False]
bounds = ot.Interval(lowerBound, upperBound, finiteLowerBound, finiteUpperBound)

In [4]:

# Create the starting point of the research
# For mu : the first point
# For sigma : a value evaluated from the two first data
mu0 = sample[0][0]
sigma0 = m.sqrt((sample[1][0] - sample[0][0]) * (sample[1][0] - sample[0][0]))
startingPoint = [mu0, sigma0]
print(startingPoint)

[4.912302476828147, 2.811562130153132]

In [5]:

# Create the estimator from a parametric pdf
pdf = ot.Normal()
factory = ot.MaximumLikelihoodFactory(pdf)
factory.setOptimizationBounds(bounds)

In [6]:

# Set the starting point via the solver
solver = factory.getOptimizationAlgorithm()
solver.setStartingPoint(startingPoint)
factory.setOptimizationAlgorithm(solver)

In [7]:

# Estimate the parametric model
distribution = factory.build(sample)
print(distribution)

Normal(mu = 3.94055, sigma = 1.48893)