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EnglishWe 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.
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| Titolo: | Markov chain monte carlo model determination for gaussian DAG models | |
| Autori: | ||
| Data di pubblicazione: | 2004 | |
| Rivista: | ||
| Abstract: | 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. | |
| Handle: | http://hdl.handle.net/11571/106154 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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