1. Limited time only! Sign up for a free 30min personal tutor trial with Chegg Tutors
    Dismiss Notice
Dismiss Notice
Join Physics Forums Today!
The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

Constraints on matrix-variate normal distributions

  1. Jul 12, 2012 #1
    Hello, all.

    I'm wondering about matrix-variate normal distributions. I know they normally assume an n x p random matrix, X, and associated row and column covariance matrices Omega and Sigma, but I'm wondering how the probability density function changes if X is comprised of a square, symmetric matrix that still has covariances among the rows and columns.

    Can it be shown that even given these covariances, the matrix can be vectorized without loss of information?

    Is it the case for this constrained example that the row and column covariance matrices are equal, and so the determinant in the denominator of the right-hand side term outside the exponential is multiplied by itself?

    I'm interested in maximum likelihood estimation under this simplified example.

    Thanks!
     
  2. jcsd
Know someone interested in this topic? Share this thread via Reddit, Google+, Twitter, or Facebook

Can you offer guidance or do you also need help?
Draft saved Draft deleted



Similar Discussions: Constraints on matrix-variate normal distributions
  1. Constraint Forces (Replies: 4)

  2. Constraint Force (Replies: 1)

  3. Moving constraints (Replies: 1)

Loading...