programming assignment in Python

(b) The property in (a) can be extended to the multivariate normal case. Suppose that for every k E (N + 1.N+M}. {Y1... Yn Ye) is multivariate normal with mean vector = and a (N+ 1) (N+1) covariance matrix , where the covariance between Y, and Y, (denoted by $_) has the following form: Dy = 0; exp(- -) + 26.), Vij 22 where az is a scale factor, is called the lengthscale, o is some positive constant (usually called the noise parame ter), and is the delta function (i.. dis = lifi =j and 8 = 0ifi #1). Given that Yi = yi Yx = ys, it can be shown that the conditional distribution of Y, is normal with mean (11)[K(TNF)+o-1 and variance K{Ix.xx) - K(**.31_w)[KIN. 11.) +011-K(+18, 12), where K(12.1) = S, is a scalar. K(+) = ( Sun) is a x N vector KEUNIN) is an N * N matrix with the (:)-th entry equal to . . I is an identity matrix of size N N. Ka) is the transpose of R(+1). .: El is an N x 1 vector Based on the above conditional distribution, please write a program (0.8. in Python or MATLAB) to find the predictive distributions of the outputs of the test query points (*N****+a)What is the prediction result of the testing dataset under a = 1,0 =0.1.1 = 0.5? What is the prediction result of the testing dataset if is set to be 0.1 instead? How about