Imperial College London

ProfessorMarkGirolami

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics
 
 
 
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Contact

 

m.girolami Website

 
 
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Location

 

539Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Stathopoulos:2013:10.1098/rsta.2011.0541,
author = {Stathopoulos, V and Girolami, MA},
doi = {10.1098/rsta.2011.0541},
journal = {Philos Trans A Math Phys Eng Sci},
title = {Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.},
url = {http://dx.doi.org/10.1098/rsta.2011.0541},
volume = {371},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.
AU - Stathopoulos,V
AU - Girolami,MA
DO - 10.1098/rsta.2011.0541
PY - 2013///
SN - 1364-503X
TI - Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.
T2 - Philos Trans A Math Phys Eng Sci
UR - http://dx.doi.org/10.1098/rsta.2011.0541
UR - https://www.ncbi.nlm.nih.gov/pubmed/23277599
VL - 371
ER -