Dynamic Non-Parametric Mixture Models and The Recurrent Chinese Restaurant Process

author: Eric P. Xing, School of Computer Science, Carnegie Mellon University
published: Aug. 4, 2008,   recorded: July 2008,   views: 5289


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Dirichlet process mixture models provide a °exible Bayesian framework for estimating a distribution as an in¯nite mixture of simpler distributions that could identify latent classes in the data [1]. However the full exchangeability assumption they employ makes them an unappealing choice for modeling longitudinal data such as text, audio and video streams that can arrive or accumulate as epochs, where data points inside the same epoch can be assumed to be fully exchangeable, whereas across the epochs both the structure (i.e., the number of mixture components) and the parameteriza- tions of the data distributions can evolve and therefore unexchangeable.

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Reviews and comments:

Comment1 korio_james, October 23, 2023 at 9:33 a.m.:

Greetings! I found your lecture very useful from the point of view of management and logistics for the restaurant business. The Chinese model is more complex because it usually takes into account a very large flow of visitors. Every little thing that speeds up the process is important here.

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