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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...
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| Published in: | American journal of political science 2000-04, Vol.44 (2), p.375-404 |
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| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c435t-ea3b1156622c69c72b7c98cd26d3a3a57eaf01137d4aa150cd5db8c68c86e3013 |
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| container_end_page | 404 |
| container_issue | 2 |
| container_start_page | 375 |
| container_title | American journal of political science |
| container_volume | 44 |
| creator | Jackman, Simon |
| description | 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. |
| doi_str_mv | 10.2307/2669318 |
| format | article |
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| ispartof | American journal of political science, 2000-04, Vol.44 (2), p.375-404 |
| issn | 0092-5853 1540-5907 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_60805642 |
| source | International Bibliography of the Social Sciences (IBSS); JSTOR Archival Journals and Primary Sources Collection; Worldwide Political Science Abstracts |
| 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 |
| title | Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo |
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