HIW Sampler is an implementation of an efficient, direct simulation methods for hyper-inverse Wishart distributions
arising in Gaussian graphical models, as described and exemplified in
The current code is in Matlab, and is used as in the example below. The data set used in the example in the above
paper and this demo example is also in the download directory.
The research and development underlying HIW Sampler was supported, in part, by the National
Science Foundation (grants DMS-0102227 and 0342172).
Any opinions, findings and conclusions or recomendations expressed in this work
are those of the authors and do not necessarily reflect the views of the NSF or NIH.
This software is made freely available to any interested user. The authors can provide no support nor assistance with implementations beyond the details and examples here, nor extensions of the code for other purposes. The download has been tested to confirm all details are operational as described here.
It is understood by the user that neither the authors nor Duke University bear any responsibility nor assume any liability for any end-use of this software. It is expected that appropriate credit/acknowledgement be given should the software be included as an element in other software development or in publications.
HIW Sampler developed by: Carlos Carvalho - Helénè Massam - Mike West