代写BIC留学生、代做LASSO method、代写Matlab代写、Matlab程序设计调试-代写Matlab编程

Question 3

Write a Matlab code to implement model selection based on the LASSO method (use the Matlab

quadprog function). Please select the parameter X by finding the minimum of BIC[

on a grid of X

values.

Apply your code to the data shown in the table below. Consider the full model 8 = :7 + :\ +:] + :^_ + :`a + :6b] + :_ + :`a + :6b + <, and evaluate

BIC[ on the grid C0,0.001,0.002, … ,1F of X values .

Compare your results to the Bayesian variable selection method described in the slides. Set c =

10, c7 = 0, G = 0.05, , = 0.5, and (d) = ∏ 0.5f$0.5f$ .

In addition, compare with model selection results obtained with the PRESS and BIC criteria.

Question 4:

Write a Matlab code to fit a stationary (implies mean and variance are constants) Gaussian

random field model with Gaussian correlation function and compute its posterior mean and 95%

interval predictions (for simplicity, you may write a code that works just for the case of a single

real input ). Apply your code to the data below, and plot the data, posterior mean, 95%

prediction intervals, and the true function q = sin (7) in the same figure. Please report the

maximum likelihood estimates of the prior mean :, prior variance

, and parameter Q in the

Gaussian correlation function.

0 sin(7 × 0)

0.2 sin(7 × 0.2)

0.4 sin(7 × 0.4)

0.6 sin(7 × 0.6)

0.8 sin(7 × 0.8)

1 sin(7 × 1)