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.
(2014)
Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach
in 31st International Conference on Machine Learning, ICML 2014
Rainforth T.
(2016)
Interacting particle markov chain monte carlo
in 33rd International Conference on Machine Learning, ICML 2016
Lienart T.
(2015)
Expectation particle belief propagation
in Advances in Neural Information Processing Systems
Paige B.
(2014)
Asynchronous anytime sequential Monte Carlo
in Advances in Neural Information Processing Systems
Hasenclever Leonard
(2015)
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
in arXiv e-prints
Vollmer Sebastian J.
(2015)
(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
in arXiv e-prints
Agapiou Sergios
(2014)
Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models
in arXiv e-prints
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Yildirim S
(2013)
An Online Expectation-Maximization Algorithm for Changepoint Models
in Journal of Computational and Graphical Statistics
Bardenet R
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
Bouchard-Côté A
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
in Journal of the American Statistical Association
Del Moral P
(2015)
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
in SIAM Journal on Control and Optimization
A Bouchard-Côté
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
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 |