A Direct Sampler for G-Wishart Variates
Abstract
The G-Wishart distribution is the conjugate prior for precision matrices that encode the conditional independencies of a Gaussian graphical model. While the distribution has received considerable attention, posterior inference has proven computationally challenging, in part due to the lack of a direct sampler. In this note, we rectify this situation. The existence of a direct sampler offers a host of new possibilities for the use of G-Wishart variates. We discuss one such development by outlining a new transdimensional model search algorithm--which we term double reversible jump--that leverages this sampler to avoid normalizing constant calculation when comparing graphical models. We conclude with two short studies meant to investigate our algorithm's validity.
Cite
@article{arxiv.1304.1350,
title = {A Direct Sampler for G-Wishart Variates},
author = {Alex Lenkoski},
journal= {arXiv preprint arXiv:1304.1350},
year = {2013}
}