Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models

Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models (1996)

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by Petros Dellaportas , Jonathan J. Forster

Venue:	Biometrika
Citations:	46 - 6 self

BibTeX

@ARTICLE{Dellaportas96markovchain,
    author = {Petros Dellaportas and Jonathan J. Forster},
    title = {Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models},
    journal = {Biometrika},
    year = {1996},
    volume = {86},
    pages = {615--633}
}

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Abstract

this paper, we will only consider undirected graphical models. For details of Bayesian model selection for directed graphical models see Madigan et al (1995). An (undirected) graphical model is determined by a set of conditional independence constraints of the form `fl 1 is independent of fl 2 conditional on all other fl i 2 C'. Graphical models are so called because they can each be represented as a graph with vertex set C and an edge between each pair fl 1 and fl 2 unless fl 1 and fl 2 are conditionally independent as described above. Darroch, Lauritzen and Speed (1980) show that each graphical log-linear model is hierarchical, with generators given by the cliques (complete subgraphs) of the graph. The total number of possible graphical models is clearly given by 2 (

Citations

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