"An Unbiased and Scalable Monte Carlo Method for Bayesian Inference for Big Data"

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• Dr Murray Pollock, University of Warwick
• Tuesday 12 January 2016, 14:30-15:30
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge.

If you have a question about this talk, please contact Alison Quenault.

Abstract: This talk will introduce novel methodology for exploring posterior distributions by modifying methodology for exactly (without error) simulating diffusion sample paths – the Scalable Langevin Exact Algorithm (ScaLE). This new method has remarkably good scalability properties (among other interesting 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.

This talk is part of the MRC Biostatistics Unit Seminars series.

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