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

@article{citeulike:116388, abstract = {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.}, author = {Jackman, Simon}, citeulike-article-id = {116388}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/2669318}, citeulike-linkout-1 = {http://www.jstor.org/stable/2669318}, doi = {10.2307/2669318}, journal = {American Journal of Political Science}, keywords = {bayesian, political\_science, statistics}, number = {2}, pages = {375--404}, posted-at = {2005-03-07 18:06:03}, priority = {2}, title = {Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo}, url = {http://dx.doi.org/10.2307/2669318}, volume = {44}, year = {2000} }

TY - JOUR ID - citeulike:116388 L3 - citeulike-article-id:116388 N2 - 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. IS - 2 JF - American Journal of Political Science EP - 404 TI - Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo VL - 44 SP - 375 KW - bayesian KW - political_science KW - statistics AU - Jackman, Simon PY - 2000/// UR - http://dx.doi.org/10.2307/2669318 ER -