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Bayesian Variable Selection Using the Gibbs Sampler
| Content Provider | Scilit |
|---|---|
| Author | Dey, Dipak K. Ghosh, Sujit K. Mallick, Bani K. |
| Copyright Year | 2000 |
| Description | In a Bayesian analysis of a generalized linear model, model uncertainty may be incorporated coherently by specifying prior probabilities for plausible models and calculating posterior probabilities using where m denotes the model, M is the set of all models under consideration, f(m) is the prior probability of model m. The observed data y contribute to the posterior model probabilities through f(y/m), the marginal likelihood calculated using f(y/m) =I f(y/m,/3m)f(f3m/m)df3m where f(f3m/m) is the conditional prior distribution of f3m, the model parameters for model m and f(y/m, f3m) is the likelihood of the data y under model m. Book Name: Generalized Linear Models |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2006-0-08981-9&isbn=9780429182402&doi=10.1201/9781482293456-25&format=pdf |
| Ending Page | 304 |
| Page Count | 14 |
| Starting Page | 291 |
| DOI | 10.1201/9781482293456-25 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2000-05-25 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Generalized Linear Models Statistics and Probability Uncertainty Sampler Bayesian Plausible Coherently Specifying F3m/m |
| Content Type | Text |
| Resource Type | Chapter |
