Bayesian Inference for Big Data with Stochastic Gradient Markov Chain Monte Carlo
Lead Research Organisation:
University of Bristol
Department Name: Mathematics
Abstract
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Organisations
People |
ORCID iD |
| Christophe Andrieu (Principal Investigator) |
Publications
Andrieu C
(2018)
Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers
in Bernoulli
Andrieu C
(2016)
Establishing some order amongst exact approximations of MCMCs
in The Annals of Applied Probability
Andrieu C
(2016)
On random- and systematic-scan samplers
in Biometrika
Andrieu Christophe
(2016)
Sampling normalizing constants in high dimensions using inhomogeneous diffusions
in arXiv e-prints
| 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 |