Bayesian MAP Model Selection of Chain Event Graphs
| dc.creator | Freeman, Guy | |
| dc.creator | Smith, Jim Q. | |
| dc.date | 2009-04-06 | |
| dc.date.accessioned | 2026-07-07T13:00:53Z | |
| dc.date.available | 2026-07-07T13:00:53Z | |
| dc.description | The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this paper we demonstrate how with complete sampling, conjugate closed form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity assumptions characterise the product Dirichlet prior on this class of models. We demonstrate our techniques using two educational examples. | |
| dc.description | 19 pages, 6 figures, 1 table Submitted to Journal of Multivariate Analysis | |
| dc.identifier | https://arxiv.org/abs/0904.0977 | |
| dc.identifier | http://arxiv.org/abs/0904.0977 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/225982 | |
| dc.subject | Methodology | |
| dc.subject | Machine Learning | |
| dc.title | Bayesian MAP Model Selection of Chain Event Graphs | |
| dc.type | text |