This paper deals with the deΓΏnition of the Bayes factor (BF) for non-dominated statistical models, where the ordinary likelihood function is not deΓΏned. A general deΓΏnition of BF is proposed, which also covers dominated models; its main properties are examined and its practical use discussed through
Bayes factors for zero partial covariances
β Scribed by Paolo Giudici
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 598 KB
- Volume
- 46
- Category
- Article
- ISSN
- 0378-3758
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In the exponential regression model, Bayesian inference concerning the non-linear regression parameter p has proved extremely difficult. In particular, standard improper diffuse priors for the usual parameters lead to an improper posterior for the non-linear regression parameter. In a recent paper Y
## Abstract The Bayes factor is a summary measure that provides an alternative to the __P__βvalue for the ranking of associations, or the flagging of associations as βsignificantβ. We describe an approximate Bayes factor that is straightforward to use and is appropriate when sample sizes are large.