makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R
Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian hierarchical models. This is particularly important when there are random effects that are hard to identify based on observed data. The challenge lies in understanding and controlling the joint influence of the p...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-08-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4686 |
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| _version_ | 1846101678162444288 |
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| author | Ingeborg Hem Geir-Arne Fuglstad Andrea Riebler |
| author_facet | Ingeborg Hem Geir-Arne Fuglstad Andrea Riebler |
| author_sort | Ingeborg Hem |
| collection | DOAJ |
| description |
Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian hierarchical models. This is particularly important when there are random effects that are hard to identify based on observed data. The challenge lies in understanding and controlling the joint influence of the priors for the variance parameters, and makemyprior is an R package that guides the formulation of joint prior distributions for variances. A joint prior distribution is constructed based on a hierarchical decomposition of the total variance in the model along a tree, and takes the entire model structure into account. Users input their prior beliefs or express ignorance at each level of the tree. Prior beliefs can be general ideas about reasonable ranges of variance values and need not be detailed expert knowledge. The constructed priors lead to robust inference and guarantee proper posteriors. A graphical user interface facilitates construction and assessment of different choices of priors through visualization of the tree and joint prior. The package aims to expand the toolbox of applied researchers and make priors an active component in their Bayesian workflow.
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| format | Article |
| id | doaj-art-1aaab5706b7a4ee2b831b97995d69df7 |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-1aaab5706b7a4ee2b831b97995d69df72024-12-29T00:12:42ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-08-01110110.18637/jss.v110.i03makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in RIngeborg Hem0Geir-Arne Fuglstad1Andrea Riebler2Norwegian University of Science and TechnologyNorwegian University of Science and TechnologyNorwegian University of Science and Technology Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian hierarchical models. This is particularly important when there are random effects that are hard to identify based on observed data. The challenge lies in understanding and controlling the joint influence of the priors for the variance parameters, and makemyprior is an R package that guides the formulation of joint prior distributions for variances. A joint prior distribution is constructed based on a hierarchical decomposition of the total variance in the model along a tree, and takes the entire model structure into account. Users input their prior beliefs or express ignorance at each level of the tree. Prior beliefs can be general ideas about reasonable ranges of variance values and need not be detailed expert knowledge. The constructed priors lead to robust inference and guarantee proper posteriors. A graphical user interface facilitates construction and assessment of different choices of priors through visualization of the tree and joint prior. The package aims to expand the toolbox of applied researchers and make priors an active component in their Bayesian workflow. https://www.jstatsoft.org/index.php/jss/article/view/4686 |
| spellingShingle | Ingeborg Hem Geir-Arne Fuglstad Andrea Riebler makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R Journal of Statistical Software |
| title | makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R |
| title_full | makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R |
| title_fullStr | makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R |
| title_full_unstemmed | makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R |
| title_short | makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R |
| title_sort | makemyprior intuitive construction of joint priors for variance parameters in r |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4686 |
| work_keys_str_mv | AT ingeborghem makemypriorintuitiveconstructionofjointpriorsforvarianceparametersinr AT geirarnefuglstad makemypriorintuitiveconstructionofjointpriorsforvarianceparametersinr AT andreariebler makemypriorintuitiveconstructionofjointpriorsforvarianceparametersinr |