# Sampling covariance matrix using Gibbs sampling

I am sampling covariance matrix from a Inverse Wishart distribution. In one dimensional case, after doing sufficient iterations I am taking the mode value for variance (after removing the burn-in values). How to do the same in a multivariate case?

• 1) Do you mean Wishart or Inverse Wishart? – Simon Byrne Dec 6 '10 at 11:07
• 2) What do you mean by "maximum occurrence"? Are you trying to find the mode (the point of highest density)? – Simon Byrne Dec 6 '10 at 11:10
• Sorry for being vague, I have modified the question. – Saurabh Saxena Dec 7 '10 at 4:26

Covariance matrix for 1-dim case reduces to the variance. Wishart Distribution (or Inv wishart distribution depending on your formulation) is a prior of covariance matrices, which for dimensions $\geq$ 2 correspond to multivariate case.