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

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Paige B. (2014) Asynchronous anytime sequential Monte Carlo in Advances in Neural Information Processing Systems

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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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Teh, YW (2015) Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

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

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Yildirim S (2013) An Online Expectation-Maximization Algorithm for Changepoint Models in Journal of Computational and Graphical Statistics

 
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