Thompson Posterior Update

Problem

Update a Beta-Bernoulli posterior after observing successes and failures. Return [alpha + successes, beta + failures].

Hint

The Beta prior is conjugate to the Bernoulli likelihood, so updating is just counting.

case 1

in: [1,1,3,2]

expected: [4,3]

case 2

in: [0.5,0.5,0,0]

expected: [0.5,0.5]

case 3

in: [2,5,0,4]

expected: [2,9]

Starter codePython

def thompson_posterior_update(alpha, beta, successes, failures):
    # Your code here
    pass

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