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

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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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In person in the Max Perutz Lecture Theatre (CB2 0QH) and via Zoom, link: https://mrc-lmb-cam-ac-uk.zoom.us/j/92910980652?pwd=U2V6MGFOOU9kbmRnckNaU05HaXl4QT09 W405 https://ukri.zoom.us/j/92025631637;BAS seminar room 1

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