9.2. Sampling bias a la Cameron-Martin. We consider a Brownian motion with drift 0 = 1: Be = Bett . (a) Generate a sample S of 100,000 paths for (By,t E [0, 1]) using a 0.01 dis- cretization. (b) Using the function numpy . random . choice, sample 1,000 paths in S not uni- formly but proportionally to their weight: M(B) = e-Bi+1/2 Again you will need to normalize the weights M(B) so that the sum over the 100,000 paths is 1. Let's call this new sample S. (c) Draw the histogram of B1/2 on the sample S. It should look like a Gaussian PDF with mean 0 and variance 1/2