TY - JOUR
T1 - Stratified Gaussian graphical models
AU - Nyman, H
AU - Pensar, Johan
AU - Corander, J
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Context-specific independence
KW - Multivariate normal distribution
KW - Bayesian model learning
KW - Gaussian graphical model
KW - Context-specific independence
KW - Multivariate normal distribution
KW - Bayesian model learning
KW - Gaussian graphical model
KW - Context-specific independence
KW - Multivariate normal distribution
KW - Bayesian model learning
KW - Gaussian graphical model
U2 - 10.1080/03610926.2015.1105979
DO - 10.1080/03610926.2015.1105979
M3 - Artikel
SN - 0361-0926
VL - 46
SP - 5556
EP - 5578
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 11
ER -