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Bayesian analysis of binary and polychotomous response data
Abstract A vast literature in statistics, biometrics, and econometrics is concerned with the analysis of binary and polychotomous response data. The classical approach fits a categorical responseExpand
Marginal Likelihood from the Gibbs Output
Abstract In the context of Bayes estimation via Gibbs sampling, with or without data augmentation, a simple approach is developed for computing the marginal density of the sample data (marginalExpand
Stochastic Volatility: Likelihood Inference And Comparison With Arch Models
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effectiveExpand
Understanding the Metropolis-Hastings Algorithm
Abstract We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation ofExpand
Bayesian Model Choice Via Markov Chain Monte Carlo Methods
SUMMARY Markov chain Monte Carlo (MCMC) integration methods enable the fitting of models of virtually unlimited complexity, and as such have revolutionized the practice of Bayesian data analysis.Expand
Estimation and comparison of multiple change-point models
This paper provides a new Bayesian approach for models with multiple change points. The centerpiece of the approach is a formulation of the change-point model in terms of a latent discrete stateExpand
Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts
We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved two-state indicator variable that follows a MarkovExpand
Marginal Likelihood From the Metropolis–Hastings Output
This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons. The approach extends and completes the method presented in Chib (1995) byExpand
Analysis of multivariate probit models
SUMMARY This paper provides a practical simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated byExpand
Markov chain Monte Carlo methods for stochastic volatility models
This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenousExpand
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