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Activity Number: 463
Type: Invited
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #314319
Title: An Unbiased and Scalable Monte Carlo Method for Bayesian Inference for Big Data
Author(s): Murray Pollock* and Paul Fearnhead and Adam Michael Johansen and Gareth O. Roberts
Companies: University of Warwick and Lancaster University and University of Warwick and University of Warwick
Keywords: Exact simulation ; Langevin diffusion ; Quasi-stationarity ; Big data
Abstract:

This talk will introduce a new methodology for exploring posterior distributions by modifying methodology for exactly (without error) simulating diffusion sample paths. This new method has remarkably good scalability properties as the size of the data set increases (it has sub-linear cost, and potentially no cost), and therefore is a natural candidate for "Big Data" inference.


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

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