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
Yildirim S
(2013)
An Online Expectation-Maximization Algorithm for Changepoint Models
in Journal of Computational and Graphical Statistics
Bardenet R.
(2014)
Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach
in 31st International Conference on Machine Learning, ICML 2014
Bishop A
(2014)
Distributed Nonlinear Consensus in the Space of Probability Measures
in IFAC Proceedings Volumes
Paige B.
(2014)
Asynchronous anytime sequential Monte Carlo
in Advances in Neural Information Processing Systems
Agapiou Sergios
(2014)
Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models
in arXiv e-prints
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
Lienart T
(2015)
Expectation Particle Belief Propagation
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 |