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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Organisations
People |
ORCID iD |
| Arnaud Doucet (Principal Investigator) |
Publications
Caron Francois
(2017)
Generalized Polya Urn for Time-Varying Pitman-Yor Processes
in JOURNAL OF MACHINE LEARNING RESEARCH
Lienart T.
(2015)
Expectation particle belief propagation
in Advances in Neural Information Processing Systems
Lienart T
(2015)
Expectation Particle Belief Propagation
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Hasenclever Leonard
(2017)
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
in JOURNAL OF MACHINE LEARNING RESEARCH
Vollmer S
(2015)
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
in SIAM/ASA Journal on Uncertainty Quantification
Wang L
(2016)
Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method
in Journal of the American Statistical Association
| 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 |