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Comparison of Two MCMC Algorithms for Hierarchical Mixture Models

Title: A Comparison of Two MCMC Algorithms for Hierarchical Mixture Models.
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Name(s): Almond, Russell, author
Type of Resource: text
Genre: Text
Date Issued: 2014-01-01
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Mixture models form an important class of models for unsupervised learning, allowing data points to be assigned labels based on their values. However, standard mixture models procedures do not deal well with rare components. For example, pause times in student essays have different lengths depending on what cognitive processes a student engages in during the pause. However, instances of student planning (and hence very long pauses) are rare, and thus it is difficult to estimate those parameters from a single student’s essays. A hierarchical mixture model eliminates some of those problems, by pooling data across several of the higher level units (in the example students) to estimate parameters of the mixture components. One way to estimate the parameters of a hierarchical mixture model is to use MCMC. But these models have several issues such as non-identifiability under label switching that make them difficultcult to estimate just using off-the-shelf MCMC tools. This paper looks at the steps necessary to estimate these models using two popular MCMC packages: JAGS (random walk Metropolis algorithm) and Stan (Hamiltonian Monte Carlo). JAGS, Stan and R code to estimate the models and model fit statistics are published along with the paper.
Identifier: FSU_libsubv1_scholarship_submission_1472577781 (IID)
Keywords: Mixture models, Markov Chain Monte Carlo, JAGS, Stan, WAIC
Publication Note: Workshop from the Bayesian Modeling Application Workshop at the Uncertainty in Artificial Intelligence Conference
Preferred Citation: Almond, R. (2014). A Comparison of Two MCMC Algorithms for Hierarchical Mixture Models. In Kathryn Laskey, James H. R. Jones, & Russell Almond (Eds.), Bayesian Modeling Application Workshop at the Uncertainty in Artificial Intelligence Conference, Quebec City, Canada (pp. 1-19). CEUR. Retrieved from http://ceur-ws.org/Vol-1218/bmaw2014_paper_1.pdf
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1472577781
Owner Institution: FSU
Is Part Of: Bayesian Modeling Application Workshop at the Uncertainty in Artificial Intelligence Conference.

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Almond, R. (2014). A Comparison of Two MCMC Algorithms for Hierarchical Mixture Models. Bayesian Modeling Application Workshop At The Uncertainty In Artificial Intelligence Conference. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1472577781