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
Bardenet R.
(2017)
On Markov chain Monte Carlo methods for tall data
in Journal of Machine Learning Research
Kantas N
(2015)
On Particle Methods for Parameter Estimation in State-Space Models
in Statistical Science
Bouchard-Côté A
(2015)
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Perrone V
(2016)
Poisson Random Fields for Dynamic Feature Models
Alenlöv J
(2016)
Pseudo-Marginal Hamiltonian Monte Carlo
Lu X.
(2017)
Relativistic Monte Carlo
in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Yildirim S
(2018)
Scalable Monte Carlo inference for state-space models
Bouchard-Côté A
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
A Bouchard-Côté
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
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 |