Inference for Diffusions and Related Processes
Lead Research Organisation:
Lancaster University
Department Name: Mathematics and Statistics
Abstract
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Organisations
Publications
Fearnhead P
(2014)
Inference for reaction networks using the linear noise approximation.
in Biometrics
Jenkins PA
(2015)
TRACTABLE DIFFUSION AND COALESCENT PROCESSES FOR WEAKLY CORRELATED LOCI.
in Electronic journal of probability
SERMAIDIS G
(2012)
Markov Chain Monte Carlo for Exact Inference for Diffusions
in Scandinavian Journal of Statistics
| Description | Monte Carlo methods are commonplace in statistics. Standard Monte Carlo methods are based around discrete-time stochastic processes. We have discovered a new class of Monte Carlo algorithms which work in continuous-time. The motivation for this was to allow inference for continuous-time stochastic models, such as diffusion processes. However it has the potential to be used much more widely. |
| Exploitation Route | This continuous-time Monte Carlo algorithm has the potential to perform Bayesian analysis in a way that scales better to Big Data. This idea is currently being developed as part of the EPSRC funded grant on intractable likelihood. This shows great promise, and has potential impact across a wide-range of areas (where there is a need to perform Bayesian inference for large data). There is an technical report available at https://arxiv.org/abs/1609.03436; and a more informal overview of this area is available at https://arxiv.org/abs/1611.07873. The latter paper has appeared in Statistical Science. |
| Sectors | Digital/Communication/Information Technologies (including Software) |
| Description | EPSRC |
| Amount | £2,400,000 (GBP) |
| Funding ID | EP/K014463/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2013 |
| End | 12/2017 |