Work

A skew Gaussian decomposable graphical model

Year: 2013

Type: preprint

Abstract: This paper propose a novel decomposable graphical model to accommodate skew Gaussian graphical models. We encode conditional independence structure among the components of the multivariate closed skew normal random vector by means of a decomposable graph and so that the pattern of zero off-diagonal elements in the precision matrix corresponds to the missing edges of the given graph. We present conditions that guarantee the propriety of the posterior distributions under the standard noninformative priors for mean vector and precision matrix, and a proper prior for skewness parameter. The identifiability of the parameters is investigated by a simulation study. Finally, we apply our methodology to two data sets.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1309.5192

Authors: Zareifard, Hamid, Rue, Havard, Khaledi, Majid Jafari, Lindgren, Finn

Institutions: Jahrom University, Norwegian University of Science and Technology, Tarbiat Modares University, University of Bath

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2951430477

Cites: 10

Cited by:

Related to: 20