Bayesian Inference for Big Data with Stochastic Gradient Markov Chain Monte Carlo
Lead Research Organisation:
University of Bristol
Department Name: Mathematics
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 |
| Christophe Andrieu (Principal Investigator) |
Publications
Andrieu C
(2018)
Quantitative Convergence Rates for Subgeometric Markov Chains
in Journal of Applied Probability
Andrieu C
(2015)
Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
in The Annals of Applied Probability
Andrieu C
(2018)
Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers
in Bernoulli
Andrieu C
(2016)
On random- and systematic-scan samplers
in Biometrika
Andrieu C
(2016)
Establishing some order amongst exact approximations of MCMCs
in The Annals of Applied Probability
Yildirim S
(2018)
Scalable Monte Carlo inference for state-space models
| Description | Scalable algorithms to handle statistical models involving latent variables. These are models which require the specification of unobserved variables (the latent variables) which must be imputed. This may be computationally expensive, in particular for large datasets, and the methods developed address this problem. |
| Exploitation Route | The work produced is going to be made available through arxiv and will hopefully be published in the future. |
| Sectors | Healthcare Pharmaceuticals and Medical Biotechnology Retail |