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 response…
Marginal Likelihood from the Gibbs Output
- S. Chib
- Computer Science, Mathematics
- 1 December 1995
This work exploits the fact that the marginal density can be expressed as the prior times the likelihood function over the posterior density, so that Bayes factors for model comparisons can be routinely computed as a by-product of the simulation.
Stochastic Volatility: Likelihood Inference And Comparison With Arch Models
- Sangjoon Kim, N. Shephard, S. Chib
- Business
- 7 October 1996
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 effective…
Understanding the Metropolis-Hastings Algorithm
- S. Chib, E. Greenberg
- Computer Science
- 1 November 1995
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 of…
Bayesian Model Choice Via Markov Chain Monte Carlo Methods
This paper presents a framework for Bayesian model choice, along with an MCMC algorithm that does not suffer from convergence difficulties, and applies equally well to problems where only one model is contemplated but its proper size is not known at the outset.
Estimation and comparison of multiple change-point models
- S. Chib
- Economics
- 1 October 1998
Marginal Likelihood From the Metropolis–Hastings Output
- S. Chib, Ivan Jeliazkov
- Computer Science
- 1 March 2001
The proposed method is developed in the context of MCMC chains produced by the Metropolis–Hastings algorithm, whose building blocks are used both for sampling and marginal likelihood estimation, thus economizing on prerun tuning effort and programming.
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 Markov…
Analysis of multivariate probit models
- S. Chib, E. Greenberg
- Economics
- 1 June 1998
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 by…
...
...