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
Lienart T
(2015)
Expectation Particle Belief Propagation
Bouchard-Côté A
(2018)
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
Perrone V
(2016)
Poisson Random Fields for Dynamic Feature Models
Bouchard-Côté A
(2015)
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
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
| 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 |