3.2 Evaluation of integral Back to our Example 1, we write a= E[X] = E[h(Y)], where X = h(Y) and Y ~ U(0,1). In other words, although X-sampling is not directly available in python, one can use U(0,1) random generator (see numpy.random.uniform) to produce Y, then compute h(Y) for the sample Xi. DN is total number of samples Ds is the sum of samples Algorithm 1 Integral by MC - Example 1 1: procedure MCINTEGRAL(N) sto 3: for i = 1...N do 4: generate two numbers Y from U(0,1) sts+h(Y) 6: return D use numpy.random.uniform 5: Sastb D return the average Example 2 Implement Algo 1 for estimator n; Estimate MSEn) for N = 25 ..., 210 and plot log-log chart. 3.2 Evaluation of integral Back to our Example 1, we write a= E[X] = E[h(Y)], where X = h(Y) and Y ~ U(0,1). In other words, although X-sampling is not directly available in python, one can use U(0,1) random generator (see numpy.random.uniform) to produce Y, then compute h(Y) for the sample Xi. DN is total number of samples Ds is the sum of samples Algorithm 1 Integral by MC - Example 1 1: procedure MCINTEGRAL(N) sto 3: for i = 1...N do 4: generate two numbers Y from U(0,1) sts+h(Y) 6: return D use numpy.random.uniform 5: Sastb D return the average Example 2 Implement Algo 1 for estimator n; Estimate MSEn) for N = 25 ..., 210 and plot log-log chart