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
Yildirim S
(2018)
Scalable Monte Carlo inference for state-space models
Kantas N
(2015)
On Particle Methods for Parameter Estimation in State-Space Models
in Statistical Science
Vollmer S
(2015)
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
in SIAM/ASA Journal on Uncertainty Quantification
Del Moral P
(2015)
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
in SIAM Journal on Control and Optimization
Lu X.
(2017)
Relativistic Monte Carlo
in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Bouchard-Côté A
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
in Journal of the American Statistical Association
Wang L
(2016)
Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method
in Journal of the American Statistical Association
Bardenet R
On Markov chain Monte Carlo Methods for Tall Data
in Journal of Machine Learning Research
Bardenet R.
(2017)
On Markov chain Monte Carlo methods for tall data
in Journal of Machine Learning Research
Caron Francois
(2017)
Generalized Polya Urn for Time-Varying Pitman-Yor Processes
in JOURNAL OF MACHINE LEARNING RESEARCH
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 |