bridgesampling: An R Package for Estimating Normalizing Constants
By Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers. Journal of Statistical Software, 2017
316 citations recorded by the source corpus.
Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7b43ffb91b4e50e91bb526b8dec168ce1b33a8e1
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In Artificial Intelligence
- Paul-Christian Burkner
- Michael Scharkow
- Gerhard E. Tutz
- R. van de Schoot
- Milica Miočević
- Robert A. I. Davies
- Lotte Meteyard
In Statistics and Probability
- Paul-Christian Burkner
- Michael Scharkow
- Gerhard E. Tutz
- Milica Miočević
- R. van de Schoot
- Robert A. I. Davies
- Lotte Meteyard
Citations in the corpus
- Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “In brms, this is realized via bridge sampling (Meng and Wong 1996; Meng and Schilling 2002) as implemented in the bridgesampling package (Gronau et al. 2020).”