Markov chain monte carlo model determination for gaussian DAG models

We present a methodology for Bayesian model choice and averaging in Gaussian directed acyclic graphs (dags). The dimension–changing move in- volves 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.