Nonparametric Bayesian Methods: Models, Algorithms, and Applications (Lecture 3)

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• Tamara Broderick (MIT)
• Friday 17 January 2020, 15:30-17:00
• MR14 (FL.05), Pavilion F, CMS.

If you have a question about this talk, please contact J.W.Stevens.

Nonparametric Bayesian methods make use of infinite-dimensional mathematical structures to allow the practitioner to learn more from their data as the size of their data set grows. What does that mean, and how does it work in practice? In this tutorial, we’ll cover why machine learning and statistics need more than just parametric Bayesian inference. We’ll introduce such foundational nonparametric Bayesian models as the Dirichlet process and Chinese restaurant process and touch on the wide variety of models available in nonparametric Bayes. Along the way, we’ll see what exactly nonparametric Bayesian methods are and what they accomplish.

This talk is part of the CCIMI Short Course: Tamara Broderick (MIT) series.

This talk is included in these lists:

• CCIMI Short Course: Tamara Broderick (MIT)
• MR14 (FL.05), Pavilion F, CMS

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