Bayesian Inference for Big Data with Stochastic Gradient Markov Chain Monte Carlo

Lead Research Organisation: University of Oxford
Department Name: Statistics

Abstract

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Publications

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Bardenet R On Markov chain Monte Carlo Methods for Tall Data in Journal of Machine Learning Research

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Bouchard-Côté A (2018) The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method in Journal of the American Statistical Association

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Bardenet Remi (2017) On Markov chain Monte Carlo methods for tall data in JOURNAL OF MACHINE LEARNING RESEARCH

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Rainforth T. (2016) Interacting particle markov chain monte carlo in 33rd International Conference on Machine Learning, ICML 2016

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Lienart T. (2015) Expectation particle belief propagation in Advances in Neural Information Processing Systems

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Del Moral P (2015) Uniform Stability of a Particle Approximation of the Optimal Filter Derivative in SIAM Journal on Control and Optimization

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Vollmer Sebastian J. (2015) (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics in arXiv e-prints

 
Description We are studying sophisticated new statistical methods to analyze big data sets. Current methods are very computationally intensive and do not scale in presence of big data. We are developing scalable yet sophisticated techniques to extract useful information from massive datasets.
Exploitation Route There is still a lot of room for improvement, both methodologically and theoretically. So we expect over the forthcoming year to develop further our new algorithms.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Security and Diplomacy

URL http://www.stats.ox.ac.uk/~doucet/journalsbysubject.html