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
Yildirim S
(2013)
An Online Expectation-Maximization Algorithm for Changepoint Models
in Journal of Computational and Graphical Statistics
Paige B.
(2014)
Asynchronous anytime sequential Monte Carlo
in Advances in Neural Information Processing Systems
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Bardenet R.
(2014)
Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach
in 31st International Conference on Machine Learning, ICML 2014
Kantas N
(2015)
On Particle Methods for Parameter Estimation in State-Space Models
in Statistical Science
Lienart T.
(2015)
Expectation particle belief propagation
in Advances in Neural Information Processing Systems
Del Moral P
(2015)
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
in SIAM Journal on Control and Optimization
Bouchard-Côté A
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Lienart T
(2015)
Expectation Particle Belief Propagation
A Bouchard-Côté
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Bouchard-Côté A
(2015)
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Vollmer S
(2015)
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
in SIAM/ASA Journal on Uncertainty Quantification
Rainforth T.
(2016)
Interacting particle markov chain monte carlo
in 33rd International Conference on Machine Learning, ICML 2016
Wang L
(2016)
Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method
in Journal of the American Statistical Association
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
| 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 |