Loading...
Please wait, while we are loading the content...
Similar Documents
Training Samples in Objective Bayesian Model Selection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Berger, James O. Pericchi, Luis Raúl |
| Copyright Year | 2002 |
| Abstract | Central to several objective approaches to Bayesian model selection is the use of training samples (subsets of the data), so as to allow utilization of improper objective priors. The most common prescription for choosing training samples is to choose them to be as small as possible, subject to yielding proper posteriors; these are called minimal training samples. When data can vary widely in terms of either information content or impact on the improper priors, use of minimal training samples can be inadequate. Important examples include certain cases of discrete data, the presence of censored observations, and certain situations involving linear models and explanatory variables. Such situations require more sophisticated methods of choosing training samples. A variety of such methods are developed in this paper, and successfully applied in challenging situations. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ftp.isds.duke.edu/WorkingPapers/02-14.pdf |
| Alternate Webpage(s) | http://ftp.stat.duke.edu/WorkingPapers/02-14.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Appendix Bayes factor Bayesian network Censor Choose (action) Computation Discrete mathematics Eisenstein's criterion Generalization (Psychology) Genetic Selection Linear model Model selection Naive Bayes classifier Randomized algorithm Randomness Sampling (signal processing) Self-information Weight cell transformation explanation |
| Content Type | Text |
| Resource Type | Article |
