Changepoint Posterior Probability

Problem

Compute the posterior probability of a changepoint from a prior probability p and two likelihoods: p * L_change / (p * L_change + (1 - p) * L_no_change).

Return 0.0 when the denominator is zero.

Hint

This is Bayes rule written as a weighted average of the two hypotheses.

Test cases

case 1

in: [0.1,0.9,0.2]

expected: 0.33333333333333337

case 2

in: [0.5,0.5,0.5]

expected: 0.5

case 3

in: [0.2,0.8,0.1]

expected: 0.6666666666666666

case 4

in: [0,0.9,0.2]

expected: 0

case 5

in: [0.3,0,0.5]

expected: 0

Starter codePython

def changepoint_posterior_probability(prior, lik_change, lik_no_change):
    # Your code here
    pass

5 test cases · run your code against them in the editor