Abstract
The application of certain Bayesian techniques, such as the Bayes factor and model
averaging, requires the specification of prior distributions on the parameters of alternative models.
We propose a new method for constructing compatible priors on the parameters of models
nested in a given directed acyclic graph model, using a conditioning approach. We define a class
of parameterizations that is consistent with the modular structure of the directed acyclic graph
and derive a procedure, that is invariant within this class, which we name reference conditioning.
Lingua originale | English |
---|---|
pagine (da-a) | 47-61 |
Numero di pagine | 15 |
Rivista | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B STATISTICAL METHODOLOGY |
Volume | 66 |
DOI | |
Stato di pubblicazione | Pubblicato - 2004 |
Keywords
- Compatible prior
- Directed acyclic graph