Using the Barnard, Mcculloch, Meng parameterization for covariance matrices

  • James

    James - 2013-01-22

    I really like the>Barnard, McCulloch and Meng (Statistica Sinica 10(2000), 1281-1311) way of formulating priors for covariance matrices. That is they place log-normal priors on the standard deviations, and let the elements of the correlation matrix be sampled from (essentially) rescaled Betas (so that they are on [-1,1] instead of [0,1]). If S is diagonal matrix contain the standard deviations and R is the correlation matrix then we can reconstruct the covariance from the product S x R x S.

    I'd very much like to do this in JAGS, but I cannot see a way around the issues of assigning constants to stochastic nodes. Is it possible?

    Last edit: James 2013-01-22
  • Joelson

    Joelson - 2014-08-28

    Hello James,

    you found the solution to your problem?


Get latest updates about Open Source Projects, Conferences and News.

Sign up for the SourceForge newsletter:

No, thanks