# Gibbs sampling of the posterior distribution¶

We sample the from the posterior distribution of the parameters of a mixture model.

where and are unknown parameters. They are a priori i.i.d. with prior distribution . This example is drawn from Example 9.2 from Monte-Carlo Statistical methods by Robert and Casella (2004).

import openturns as ot
from openturns.viewer import View
import numpy as np

ot.RandomGenerator.SetSeed(100)


Sample data with and .

N = 500
p = 0.3

mu0 = 0.0
mu1 = 2.7
nor0 = ot.Normal(mu0, 1.0)
nor1 = ot.Normal(mu1, 1.0)
true_distribution = ot.Mixture([nor0, nor1], [1 - p, p])
observations = np.array(true_distribution.getSample(500))


Plot the true distribution.

graph = true_distribution.drawPDF()
graph.setTitle("True distribution")
graph.setXTitle("")
graph.setLegends([""])
View(graph)


Out:

<openturns.viewer.View object at 0x7f7308fad0c0>


A natural step at this point is to introduce an auxiliary (unobserved) random variable telling from which distribution was sampled.

For any nonnegative integer , follows the Bernoulli distribution with , and .

Let (resp. ) denote the number of indices such that (resp. ).

Conditionally to all and all , and are independent: follows and follows .

For any , conditionally to , and , is independent from all () and follows the Bernoulli distribution with parameter

We now sample from the joint distribution of conditionally to the using the Gibbs algorithm. We define functions that will translate a given state of the Gibbs algorithm into the correct parameters for the distributions of , , and the .

def nor0post(pt):
z = np.array(pt)[2:]
x0 = observations[z == 0]
mu0 = x0.sum() / (0.1 + len(x0))
sigma0 = 1.0 / (0.1 + len(x0))
return [mu0, sigma0]

def nor1post(pt):
z = np.array(pt)[2:]
x1 = observations[z == 1]
mu1 = x1.sum() / (0.1 + len(x1))
sigma1 = 1.0 / (0.1 + len(x1))
return [mu1, sigma1]

def zpost(pt):
mu0 = pt[0]
mu1 = pt[1]
term1 = p * np.exp(- (observations - mu1)**2 / 2)
term0 = (1.0 - p) * np.exp(- (observations - mu0)**2 / 2)
res = term1 / (term1 + term0)
# output must be a 1d list or array in order to create a PythonFunction
return res.reshape(-1)

nor0posterior = ot.PythonFunction(2 + N, 2, nor0post)
nor1posterior = ot.PythonFunction(2 + N, 2, nor1post)
zposterior = ot.PythonFunction(2 + N, N, zpost)


We can now construct the Gibbs algorithm

initialState = [0.0] * (N + 2)

sampler0 = ot.RandomVectorMetropolisHastings(
ot.RandomVector(ot.Normal()), initialState, [0], nor0posterior)
sampler1 = ot.RandomVectorMetropolisHastings(
ot.RandomVector(ot.Normal()), initialState, [1], nor1posterior)

big_bernoulli = ot.ComposedDistribution([ot.Bernoulli()] * N)

sampler2 = ot.RandomVectorMetropolisHastings(ot.RandomVector(
big_bernoulli), initialState, range(2, N + 2), zposterior)

gibbs = ot.Gibbs([sampler0, sampler1, sampler2])


Run the Gibbs algorithm

s = gibbs.getSample(10000)


Extract the relevant marginals: the first () and the second ().

posterior_sample = s[:, 0:2]


Let us plot the posterior density.

ks = ot.KernelSmoothing().build(posterior_sample)
graph = ks.drawPDF()
graph.setTitle("Posterior density")
graph.setLegendPosition("bottomright")
graph.setXTitle(r"$\mu_0$")
graph.setYTitle(r"$\mu_1$")
View(graph)

View.ShowAll()


Total running time of the script: ( 0 minutes 14.314 seconds)

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