Building Hyper Dirichlet Processes for Graphical Models
| dc.creator | Heinz, Daniel | |
| dc.date | 2008-07-22 | |
| dc.date.accessioned | 2026-07-07T09:52:03Z | |
| dc.date.available | 2026-07-07T09:52:03Z | |
| dc.description | Graphical models are used to describe the conditional independence relations in multivariate data. They have been used for a variety of problems, including log-linear models (Liu and Massam, 2006), network analysis (Holland and Leinhardt, 1981; Strauss and Ikeda, 1990; Wasserman and Pattison, 1996; Pattison and Wasserman, 1999; Robins et al., 1999);, graphical Gaussian models (Roverato and Whittaker, 1998; Giudici and Green, 1999; Marrelec and Benali, 2006), and genetics (Dobra et al., 2004). A distribution that satisfies the conditional independence structure of a graph is Markov. A graphical model is a family of distributions that is restricted to be Markov with respect to a certain graph. In a Bayesian problem, one may specify a prior over the graphical model. Such a prior is called a hyper Markov law if the random marginals also satisfy the independence constraints. Previous work in this area includes (Dempster, 1972; Dawid and Lauritzen, 1993; Giudici and Green, 1999; Letac and Massam, 2007). We explore graphical models based on a non-parametric family of distributions, developed from Dirichlet processes. | |
| dc.description | Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0807.3410 | |
| dc.identifier | http://arxiv.org/abs/0807.3410 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/165460 | |
| dc.subject | Statistics Theory | |
| dc.subject | 36E05 (Primary) 62G99 (Secondary) | |
| dc.title | Building Hyper Dirichlet Processes for Graphical Models | |
| dc.type | text |