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
This paper describes a method for estimating the marginal likelihood or Bayes fac-tors of Bayesian m...
Evaluating normalising constants is important across a range of topics in statistical learning, nota...
Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are probl...
Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the...
Computation of normalizing constants is a fundamental mathematical problem in various disciplines, p...
Abstract: In Bayesian inference, a Bayes factor is defined as the ratio of posterior odds versus pri...
In this paper we propose a new effective tool for evaluating the normalizing constant of an arbitra...
Abstract. Ratios of normalizing constants for two distributions are needed in both Bayesian statisti...
Abstract: This paper deals with a computational aspect of the Bayesian analysis of statisti-cal mode...
The computation of normalizing constants often brings (higher-dimensional) integrals, which could no...
Abstract The marginal likelihood plays an important role in many areas of Bayesian statistics such a...
This dissertation focuses on Bayesian sampling, Bayesian evidence estimation and supervised function...
AbstractBayesian variable selection often assumes normality, but the effects of model misspecificati...
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model se...
Markov chain Monte Carlo (the Metropolis-Hastings algorithm and the Gibbs sampler) is a general mult...
This paper describes a method for estimating the marginal likelihood or Bayes fac-tors of Bayesian m...
Evaluating normalising constants is important across a range of topics in statistical learning, nota...
Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are probl...
Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the...
Computation of normalizing constants is a fundamental mathematical problem in various disciplines, p...
Abstract: In Bayesian inference, a Bayes factor is defined as the ratio of posterior odds versus pri...
In this paper we propose a new effective tool for evaluating the normalizing constant of an arbitra...
Abstract. Ratios of normalizing constants for two distributions are needed in both Bayesian statisti...
Abstract: This paper deals with a computational aspect of the Bayesian analysis of statisti-cal mode...
The computation of normalizing constants often brings (higher-dimensional) integrals, which could no...
Abstract The marginal likelihood plays an important role in many areas of Bayesian statistics such a...
This dissertation focuses on Bayesian sampling, Bayesian evidence estimation and supervised function...
AbstractBayesian variable selection often assumes normality, but the effects of model misspecificati...
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model se...
Markov chain Monte Carlo (the Metropolis-Hastings algorithm and the Gibbs sampler) is a general mult...
This paper describes a method for estimating the marginal likelihood or Bayes fac-tors of Bayesian m...
Evaluating normalising constants is important across a range of topics in statistical learning, nota...
Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are probl...