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
Bouchard-Côté A
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
Bouchard-Côté A
(2015)
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Caron Francois
(2017)
Generalized Polya Urn for Time-Varying Pitman-Yor Processes
in JOURNAL OF MACHINE LEARNING RESEARCH
Del Moral P
(2015)
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
in SIAM Journal on Control and Optimization
Döbler C
(2017)
An iterative technique for bounding derivatives of solutions of Stein equations
in Electronic Journal of Probability
Hasenclever Leonard
(2015)
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
in arXiv e-prints
Kantas N
(2015)
On Particle Methods for Parameter Estimation in State-Space Models
in Statistical Science
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 |