Stratified Gaussian graphical models

Show full item record



Permalink

http://hdl.handle.net/10138/313146

Citation

Nyman , H , Pensar , J & Corander , J 2017 , ' Stratified Gaussian graphical models ' , Communications in Statistics: Theory and Methods , vol. 46 , no. 11 , pp. 5556-5578 . https://doi.org/10.1080/03610926.2015.1105979

Title: Stratified Gaussian graphical models
Author: Nyman, Henrik; Pensar, Johan; Corander, Jukka
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2017
Language: eng
Number of pages: 23
Belongs to series: Communications in Statistics: Theory and Methods
ISSN: 0361-0926
URI: http://hdl.handle.net/10138/313146
Abstract: Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivariate normal distribution, use of this model family may hide certain forms of context-specific independence that are natural to consider from an applied perspective. Such independencies have been earlier introduced to generalize discrete graphical models and Bayesian networks into more flexible model families. Here, we adapt the idea of context-specific independence to Gaussian graphical models by introducing a stratification of the Euclidean space such that a conditional independence may hold in certain segments but be absent elsewhere. It is shown that the stratified models define a curved exponential family, which retains considerable tractability for parameter estimation and model selection.
Subject: Bayesian model learning
Context-specific independence
Gaussian graphical model
Multivariate normal distribution
CONTEXT-SPECIFIC INDEPENDENCE
COVARIANCE-SELECTION
CONTINGENCY-TABLES
INFERENCE
NETWORKS
MCMC
112 Statistics and probability
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
1409.2262.pdf 591.6Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record