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Activity Number: 507 - Bayesian Data Science and Statistical Science
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #327233 Presentation
Title: Polynomial Approximate Sufficient Statistics for Scalable Bayesian Inference
Author(s): Tamara Broderick*
Companies: Massachusetts Institute of Technology
Keywords: Bayes; Bayesian; scalable; sufficient statistics; big data

Generalized linear models (GLMs) provide interpretable models for diverse data types. Probabilistic approaches, particularly Bayesian ones, allow coherent estimates of uncertainty, incorporation of prior information, and sharing of power across experiments via hierarchical modeling. In practice, however, the approximate Bayesian methods necessary for inference have either failed to scale to large data sets or failed to provide theoretical guarantees on the quality of inference. We propose a new approach based on constructing polynomial approximate sufficient statistics for GLMs (PASS-GLM). We demonstrate that our method admits a simple algorithm as well as trivial streaming and distributed extensions that do not compound error across computations. We provide theoretical guarantees on the quality of point (MAP) estimates, the approximate posterior, and posterior mean and uncertainty estimates. We validate our approach empirically in the case of logistic regression using a quadratic approximation and show competitive performance in terms of both speed and accuracy-including on an industry data set with 40 million data points and 20,000 covariates.

Authors who are presenting talks have a * after their name.

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