Starts 17 Nov 2016 11:00
Ends 17 Nov 2016 12:00
Central European Time
Cox-process representation and inference for stochastic reaction-diffusion processes
ICTP
Common area, 2nd floor, old SISSA building
Abstract:
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. On the other hand, spatio-temporal point processes offer several computational advantages from the statistical perspective, and can be coupled to dynamics via an evolving intensity f! ield. In this talk, I will discuss how joint time marginals of a stochastic reaction-diffusion process can be approximated in a mean-field sense by a spatio-temporal Cox process. The resulting approximation allows us to naturally define an approximate likelihood, which can be optimised with respect to the kinetic parameters of the model. We show on several examples from systems biology and epidemiology that the method yields consistently accurate parameter estimates, and can be used effectively for model selection.
Ref: Schnoerr, Grima and Sanguinetti, Nature Communications 7, 2016 also http://arxiv.org/abs/1601.01972
Guido Sanguinetti is a Reader in Machine Learning at the School of Informatics, University of Edinburgh. After a degree in Physics (Genova) and a PhD in Mathematics (Oxford), his interests shifted towards machine learning and computational biology, with a particular focus on devising statistical models for the dynamics of gene regulation. He has published over 60 articles in leading specialist and interdisciplinary journals, including Science, PNAS, Nature Communications, Bioinformatics, and JASA. He holds an ERC starting grant and is the recipient of the 2012 PNAS Cozzarelli Prize in Engineering and Applied Science.
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. On the other hand, spatio-temporal point processes offer several computational advantages from the statistical perspective, and can be coupled to dynamics via an evolving intensity f! ield. In this talk, I will discuss how joint time marginals of a stochastic reaction-diffusion process can be approximated in a mean-field sense by a spatio-temporal Cox process. The resulting approximation allows us to naturally define an approximate likelihood, which can be optimised with respect to the kinetic parameters of the model. We show on several examples from systems biology and epidemiology that the method yields consistently accurate parameter estimates, and can be used effectively for model selection.
Ref: Schnoerr, Grima and Sanguinetti, Nature Communications 7, 2016 also http://arxiv.org/abs/1601.01972
Guido Sanguinetti is a Reader in Machine Learning at the School of Informatics, University of Edinburgh. After a degree in Physics (Genova) and a PhD in Mathematics (Oxford), his interests shifted towards machine learning and computational biology, with a particular focus on devising statistical models for the dynamics of gene regulation. He has published over 60 articles in leading specialist and interdisciplinary journals, including Science, PNAS, Nature Communications, Bioinformatics, and JASA. He holds an ERC starting grant and is the recipient of the 2012 PNAS Cozzarelli Prize in Engineering and Applied Science.