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.
Similar content being viewed by others
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fronk, EM., Giudici, P. Markov Chain Monte Carlo model selection for DAG models. Statistical Methods & Applications 13, 259–273 (2004). https://doi.org/10.1007/s10260-004-0097-z
Issue Date:
DOI: https://doi.org/10.1007/s10260-004-0097-z