MLE for Bernoulli

Problem

Maximum likelihood estimate of the success probability from successes successes in trials independent Bernoulli trials:

p_hat = successes / trials.

Return 0.0 when trials <= 0.

Hint

The sample proportion maximizes the Bernoulli likelihood.

case 1

in: [3,10]

expected: 0.3

case 2

in: [0,5]

expected: 0

case 3

in: [5,5]

expected: 1

case 4

in: [0,0]

expected: 0

Starter codePython

def mle_bernoulli(successes, trials):
    # Your code here
    pass

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