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
A Bouchard-Côté
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Agapiou S
(2018)
Unbiased Monte Carlo: Posterior estimation for intractable/infinite-dimensional models
in Bernoulli
Alenlöv J
(2016)
Pseudo-Marginal Hamiltonian Monte Carlo
Bardenet R
On Markov chain Monte Carlo Methods for Tall Data
in Journal of Machine Learning Research
Bardenet R.
(2014)
Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach
in 31st International Conference on Machine Learning, ICML 2014
Bardenet R.
(2017)
On Markov chain Monte Carlo methods for tall data
in Journal of Machine Learning Research
Bardenet Remi
(2017)
On Markov chain Monte Carlo methods for tall data
in JOURNAL OF MACHINE LEARNING RESEARCH
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Bouchard-Côté A
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain 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 |