Markov Chain Monte Carlo model selection for DAG models

Eva-Maria Fronk () and Paolo Giudici
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Eva-Maria Fronk: Ludwig-Maximilians-University

Statistical Methods & Applications, 2004, vol. 13, issue 3, No 1, 259-273

Abstract: Abstract. We present a methodology for Bayesian model choice and averaging in Gaussian directed acyclic graphs (dags). The dimension-changing move involves adding or dropping a (directed) edge from the graph. The methodology employs the results in Geiger and Heckerman and searches directly in the space of all dags. Model determination is carried out by implementing a reversible jump Markov Chain Monte Carlo sampler. To achieve this aim we rely on the concept of adjacency matrices, which provides a relatively inexpensive check for acyclicity. The performance of our procedure is illustrated by means of two simulated datasets, as well as one real dataset.

Keywords: Graphical models; model selection; probabilistic expert systems; reversible jump MCMC algorithm (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10260-004-0097-z

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