JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 157
Type: Invited
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #314153
Title: Scaling and Generalizing Variational Inference
Author(s): David Blei*
Companies: Columbia University
Keywords: approximate posterior inference ; variational methods ; scalable computation ; Bayesian methods
Abstract:

The central computational problem of Bayesian statistics is posterior inference, the problem of approximating the conditional distribution of latent variables given observations. Approximate posterior inference algorithms have revolutionized Bayesian statistics, revealing its potential as a general-purpose language for data analysis.

Bayesian statistics, however, has not yet reached this potential. First, statisticians regularly encounter massive data, but existing approximate inference algorithms do not scale well. Second, most approximate inference algorithms must be tailored to the specific model at hand. This requires significant model-specific analysis, which precludes us from easily exploring a variety of models.

We have addressed these limitations. First, stochastic variational inference is an approximate inference algorithm for handling massive data sets. It opens the door to scalable Bayesian computation for modern data analysis. Second, Black box inference is a generic algorithm for approximating the posterior. We can apply it to many models with little model-specific derivation and few restrictions on their properties.


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

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home