Bayesian Inference for Big Data with Stochastic Gradient Markov Chain Monte Carlo
Lead Research Organisation:
University of Oxford
Department Name: Statistics
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
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
Bouchard-Côté A
(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
Bouchard-Côté A
(2015)
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Lienart T
(2015)
Expectation Particle Belief Propagation
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
Bardenet Remi
(2017)
On Markov chain Monte Carlo methods for tall data
in JOURNAL OF MACHINE LEARNING RESEARCH
Yildirim S
(2013)
An Online Expectation-Maximization Algorithm for Changepoint Models
in Journal of Computational and Graphical Statistics
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Döbler C
(2017)
An iterative technique for bounding derivatives of solutions of Stein equations
in Electronic Journal of Probability
Agapiou S
(2018)
Unbiased Monte Carlo: Posterior estimation for intractable/infinite-dimensional models
in Bernoulli
| 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 |