Legend: Palais des congrès de Montréal = CC, Le Westin Montréal = W, Intercontinental Montréal = I
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_24C | Tue, 8/6/2013, 8:30 AM - 5:00 PM | W-St. Antoine A | |
| Applied Bayesian Nonparametric Mixture Modeling — Continuing Education Course | |||
| ASA , Section on Bayesian Statistical Science | |||
| Instructor(s): Athanasios Kottas, University of California, Santa Cruz, Abel Rodriguez, University of California, Santa Cruz | |||
| Bayesian methods are central to the application of modern statistical modeling in a wide variety of fields. Bayesian nonparametric and semiparametric methods are receiving increased attention in the literature as they considerably expand the flexibility of Bayesian models. This one-day course will provide an introduction to Bayesian nonparametric methods, with emphasis on modeling approaches employing nonparametric mixtures and with a focus on applications. The course will start by motivating Bayesian nonparametric modeling and providing an overview of nonparametric prior models for spaces of random functions. The main focus will be on models based on the Dirichlet process, a nonparametric prior for distribution functions. Particular emphasis will be placed on Dirichlet process mixtures, which provide a flexible framework for nonparametric modeling. We will discuss methodological details, computational techniques for posterior inference, and applications of Dirichlet process mixture models. Examples will be drawn from fields such as density estimation, nonparametric regression, hierarchical generalized linear models, survival analysis, and spatial statistics. The course targets students or professionals with background in Bayesian modeling and inference. Sufficient preparation will include statistics training to the M.S. level and some exposure to Bayesian hierarchical modeling and computation. | |||
2013 JSM Online Program Home
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