Bayesian auxiliary variable models for binary and multinomial regression
In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conve...
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| Format: | Journal article |
| Sprache: | English |
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2006
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| _version_ | 1826282327489118208 |
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| author | Holmes, C Held, L |
| author_facet | Holmes, C Held, L |
| author_sort | Holmes, C |
| collection | OXFORD |
| description | In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for inference in Bayesian logistic regression, including covariate set uncertainty. Finally, we show how the logistic method is easily extended to multinomial regression models. All of the algorithms are fully automatic with no user set parameters and no necessary Metropolis-Hastings accept/reject steps. © 2006 International Society for Bayesian Analysis. |
| first_indexed | 2024-03-07T00:42:10Z |
| format | Journal article |
| id | oxford-uuid:836d1da8-0202-4c8c-a229-32b80d6fe742 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:42:10Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:836d1da8-0202-4c8c-a229-32b80d6fe7422022-03-26T21:44:01ZBayesian auxiliary variable models for binary and multinomial regressionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:836d1da8-0202-4c8c-a229-32b80d6fe742EnglishSymplectic Elements at Oxford2006Holmes, CHeld, LIn this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for inference in Bayesian logistic regression, including covariate set uncertainty. Finally, we show how the logistic method is easily extended to multinomial regression models. All of the algorithms are fully automatic with no user set parameters and no necessary Metropolis-Hastings accept/reject steps. © 2006 International Society for Bayesian Analysis. |
| spellingShingle | Holmes, C Held, L Bayesian auxiliary variable models for binary and multinomial regression |
| title | Bayesian auxiliary variable models for binary and multinomial regression |
| title_full | Bayesian auxiliary variable models for binary and multinomial regression |
| title_fullStr | Bayesian auxiliary variable models for binary and multinomial regression |
| title_full_unstemmed | Bayesian auxiliary variable models for binary and multinomial regression |
| title_short | Bayesian auxiliary variable models for binary and multinomial regression |
| title_sort | bayesian auxiliary variable models for binary and multinomial regression |
| work_keys_str_mv | AT holmesc bayesianauxiliaryvariablemodelsforbinaryandmultinomialregression AT heldl bayesianauxiliaryvariablemodelsforbinaryandmultinomialregression |