Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R
This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hy...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2015-11-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/699 |
| _version_ | 1811235972071817216 |
|---|---|
| author | Stefan Zeugner Martin Feldkircher |
| author_facet | Stefan Zeugner Martin Feldkircher |
| author_sort | Stefan Zeugner |
| collection | DOAJ |
| description | This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large. |
| first_indexed | 2024-04-12T12:01:16Z |
| format | Article |
| id | doaj.art-59858f2e83154ceeb4ce36ba2458687a |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-04-12T12:01:16Z |
| publishDate | 2015-11-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-59858f2e83154ceeb4ce36ba2458687a2022-12-22T03:33:51ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602015-11-0168113710.18637/jss.v068.i04962Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for RStefan ZeugnerMartin FeldkircherThis article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large.https://www.jstatsoft.org/index.php/jss/article/view/699hyper-g priorbinomial-beta priorempirical Bayescustomized prior inclusion probabilitiesBMSR |
| spellingShingle | Stefan Zeugner Martin Feldkircher Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R Journal of Statistical Software hyper-g prior binomial-beta prior empirical Bayes customized prior inclusion probabilities BMS R |
| title | Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R |
| title_full | Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R |
| title_fullStr | Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R |
| title_full_unstemmed | Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R |
| title_short | Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R |
| title_sort | bayesian model averaging employing fixed and flexible priors the bms package for r |
| topic | hyper-g prior binomial-beta prior empirical Bayes customized prior inclusion probabilities BMS R |
| url | https://www.jstatsoft.org/index.php/jss/article/view/699 |
| work_keys_str_mv | AT stefanzeugner bayesianmodelaveragingemployingfixedandflexiblepriorsthebmspackageforr AT martinfeldkircher bayesianmodelaveragingemployingfixedandflexiblepriorsthebmspackageforr |