The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling
Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC a...
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2009/537139 |
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| _version_ | 1832565738620059648 |
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| author | Roy Levy |
| author_facet | Roy Levy |
| author_sort | Roy Levy |
| collection | DOAJ |
| description | Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models. |
| format | Article |
| id | doaj-art-b28e9d954d6e48deb28f18b1c5ee4d04 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-b28e9d954d6e48deb28f18b1c5ee4d042025-02-03T01:06:52ZengWileyJournal of Probability and Statistics1687-952X1687-95382009-01-01200910.1155/2009/537139537139The Rise of Markov Chain Monte Carlo Estimation for Psychometric ModelingRoy Levy0Division of Advanced Studies in Learning, Technology and Psychology in Education, Arizona State University, PO Box 870611, Tempe, AZ 85287-0611, USAMarkov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models.http://dx.doi.org/10.1155/2009/537139 |
| spellingShingle | Roy Levy The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling Journal of Probability and Statistics |
| title | The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling |
| title_full | The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling |
| title_fullStr | The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling |
| title_full_unstemmed | The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling |
| title_short | The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling |
| title_sort | rise of markov chain monte carlo estimation for psychometric modeling |
| url | http://dx.doi.org/10.1155/2009/537139 |
| work_keys_str_mv | AT roylevy theriseofmarkovchainmontecarloestimationforpsychometricmodeling AT roylevy riseofmarkovchainmontecarloestimationforpsychometricmodeling |