Bayes Factor from Marginal Likelihoods

Problem

The Bayes factor comparing two models is the ratio of their marginal likelihoods, BF = P(data | model 1) / P(data | model 2). Return that ratio, or 0.0 when evidence_h1 is negative or evidence_h2 is nonpositive.

Hint

A Bayes factor above 1 favors the first model.

Test cases

case 1

in: [0.02,0.01]

expected: 2

case 2

in: [0.5,0.5]

expected: 1

case 3

in: [0,0.5]

expected: 0

case 4

in: [0.3,0.1]

expected: 2.9999999999999996

case 5

in: [1,0]

expected: 0

Starter codePython

def bayes_factor_from_evidence(evidence_h1, evidence_h2):
    # Your code here
    pass

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