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
Agapiou Sergios
(2014)
Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models
in arXiv e-prints
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
(2015)
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
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
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
in Journal of the American Statistical Association
Caron Francois
(2017)
Generalized Polya Urn for Time-Varying Pitman-Yor Processes
in JOURNAL OF MACHINE LEARNING RESEARCH
Del Moral P
(2015)
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
in SIAM Journal on Control and Optimization
Döbler C
(2017)
An iterative technique for bounding derivatives of solutions of Stein equations
in Electronic Journal of Probability
Hasenclever Leonard
(2015)
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
in arXiv e-prints
Kantas N
(2015)
On Particle Methods for Parameter Estimation in State-Space Models
in Statistical Science
Lienart T
(2015)
Expectation Particle Belief Propagation
Lienart T.
(2015)
Expectation particle belief propagation
in Advances in Neural Information Processing Systems
Lu X.
(2017)
Relativistic Monte Carlo
in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Paige B.
(2014)
Asynchronous anytime sequential Monte Carlo
in Advances in Neural Information Processing Systems
Rainforth T.
(2016)
Interacting particle markov chain monte carlo
in 33rd International Conference on Machine Learning, ICML 2016
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 |