Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo

Bayesian statistics have made great strides in recent years, developing a class of methods for estimation and inference via stochastic simulation known as Markov Chain Monte Carlo (MCMC) methods. MCMC constitutes a revolution in statistical practice with effects beginning to be felt in the social sc...

Full description

Saved in:
Bibliographic Details
Published in:American journal of political science 2000-04, Vol.44 (2), p.375-404
Main Author: Jackman, Simon
Format: Article
Language:English
Subjects:
Bayesian method
Data sampling
Estimation
Inference
Linear models
Markov analysis
Markov chains
Maximum likelihood estimation
Methodology
Metropolitan areas
Missing data
Modeling
Monte Carlo simulation
Parametric models
Political science
Quantitative analysis
Railroad transportation
Sampling distributions
Simulation
Simulation and Games
Social Science
Social sciences
Statistical variance
Statistics
Stochastic models
Time series
Workshops
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Bayesian statistics have made great strides in recent years, developing a class of methods for estimation and inference via stochastic simulation known as Markov Chain Monte Carlo (MCMC) methods. MCMC constitutes a revolution in statistical practice with effects beginning to be felt in the social sciences: models long consigned to the "too hard" basket are now within reach of quantitative researchers. I review the statistical pedigree of MCMC and the underlying statistical concepts. I demonstrate some of the strengths and weaknesses of MCMC and offer practical suggestions for using MCMC in social-science settings. Simple, illustrative examples include a probit model of voter turnout and a linear regression for time-series data with autoregressive disturbances. I conclude with a more challenging application, a multinomial probit model, to showcase the power of MCMC methods.
ISSN:0092-5853
1540-5907
DOI:10.2307/2669318