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EnglishThis paper deals with the Bayesian analysis of discrete bi-directed graphical mo\-dels. A missing edge in the graph denotes marginal independence between the corresponding variables. The augmented DAG representation of the model is exploited. The augmented model is parameterised in terms of a minimal set of marginal and conditional probability parameters. Compatible priors based on product of Dirichlet Distributions are applied. The prior parameters are specified via a power prior approach. The posterior distributions of the marginal log-linear parameters are obtained using Monte Carlo simulations.
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| Titolo: | Bayesian Analysis of discrete Bi-directed Graphical Models via Augmented DAG Representation |
| Autori: | |
| Data di pubblicazione: | 2013 |
| Abstract: | This paper deals with the Bayesian analysis of discrete bi-directed graphical mo\-dels. A missing edge in the graph denotes marginal independence between the corresponding variables. The augmented DAG representation of the model is exploited. The augmented model is parameterised in terms of a minimal set of marginal and conditional probability parameters. Compatible priors based on product of Dirichlet Distributions are applied. The prior parameters are specified via a power prior approach. The posterior distributions of the marginal log-linear parameters are obtained using Monte Carlo simulations. |
| Handle: | http://hdl.handle.net/11571/746820 |
| ISBN: | 9788834325568 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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