Inference for Diffusions and Related Processes

Lead Research Organisation: Lancaster University
Department Name: Mathematics and Statistics

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.

Publications

10 25 50
publication icon
Fearnhead P (2014) Inference for reaction networks using the linear noise approximation. in Biometrics

publication icon
Fearnhead Paul (2017) Continious-time Importance Sampling: Monte Carlo Methods which Avoid Time-discretisation Error in arXiv e-prints

publication icon
Jenkins PA (2015) TRACTABLE DIFFUSION AND COALESCENT PROCESSES FOR WEAKLY CORRELATED LOCI. in Electronic journal of probability

publication icon
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