Conditional Expectation Discrete Table

Problem

For a joint distribution given as [x, y, probability] rows, E[X | Y = y0] = sum_{rows with y = y0} x p / sum_{rows with y = y0} p.

Given the rows and the target y0, return the conditional expectation.

Hint

Restrict to the matching rows and renormalize.

case 1

in: [[[0,0,0.25],[1,0,0.25],[0,1,0.25],[3,1,0.25]],0]

expected: 0.5

case 2

in: [[[2,0,0.1],[4,0,0.4],[1,1,0.5]],1]

expected: 1

case 3

in: [[[3,2,0.5],[1,2,0.5]],2]

expected: 2

Starter codePython

def conditional_expectation_discrete_table(rows, target_y):
    # Your code here
    pass

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