MCMC Burn-In Mean

Problem

Discard the first burn_in samples of a Markov chain and return the mean of the remaining samples. A negative burn_in is treated as 0, and 0.0 is returned when nothing remains.

Hint

Burn-in removes the transient before averaging.

Test cases

case 1

in: [[1,2,3,4,5],2]

expected: 4

case 2

in: [[1,2],0]

expected: 1.5

case 3

in: [[1,2,3],3]

expected: 0

case 4

in: [[],0]

expected: 0

Starter codePython

def mcmc_burn_in_mean(samples, burn_in):
    # Your code here
    pass

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