Consider the random vector X = (X_1 + X_2 X_1 – X_2)T where X_1 and X_2 are independent Gaussian random variables ~ N(1, 2), i.e. they have a mean of 1 and variance of 2. The mean vector of X is equal to A. (2 2)^T B. (2 0)T C. (0 4)^T D. (4 4)^T E. None of the above

35 6 - Consider the random vector X = (X_1 + X_2 X_1 - X_2)T where X_1 and X_2 are independent Gaussian random variables ~ N(1, 2), i.e. they have a mean of 1 and variance of 2. The mean vector of X is equal to A. (2 2)^T B. (2 0)T C. (0 4)^T D. (4 4)^T E. None of the above

This content is for Premium members only.
sign up for premium and access unlimited solutions for a month at just 5$(not renewed automatically)


images - Consider the random vector X = (X_1 + X_2 X_1 - X_2)T where X_1 and X_2 are independent Gaussian random variables ~ N(1, 2), i.e. they have a mean of 1 and variance of 2. The mean vector of X is equal to A. (2 2)^T B. (2 0)T C. (0 4)^T D. (4 4)^T E. None of the above

already a member please login