<b>bridgesampling</b>: An <i>R</i> Package for Estimating Normalizing Constants
Quentin F. Gronau, Henrik Singmann, Eric‐Jan Wagenmakers
Abstract
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 highdimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng and Wong 1996; Meng and 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.
Topics & Concepts
R packageComputer scienceComputationAlgorithmApplied mathematicsBayes' theoremConstant (computer programming)Bayesian probabilityMathematicsBayes factorArtificial intelligenceComputational scienceProgramming languageStatistical Methods and Bayesian InferenceGenetic and phenotypic traits in livestockStatistical Methods and Inference