bridgesampling: An R Package for Estimating Normalizing Constants. 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.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
- Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F.: Properties of the bridge sampler with a focus on splitting the MCMC sample (2020)
- Annis, Jeffrey; Evans, Nathan J.; Miller, Brent J.; Palmeri, Thomas J.: Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial (2019)
- Veen, Duco; Klugkist, Irene: Standard errors, priors, and bridge sampling: a discussion of Liu et al. (2019)
- Li, Le; Guedj, Benjamin; Loustau, Sébastien: A quasi-Bayesian perspective to online clustering (2018)
- Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
- Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv
- Meng, Xiao-Li; Wong, Wing Hung: Simulating ratios of normalizing constants via a simple identity: A theoretical exploration (1996)