2013 Academic Year Seminars
In this talk I will present some results on inference for continuous time stochastic processes. The type of process we will consider is the Markov Jump process (MJP), where the state of the system (a multi-dimensional vector with integer entries) evolves in continuous time with Markovian dynamics. This type of system is frequently used in modelling chemical reactions at low count numbers, or in ecological systems. In the first part of the process I'll introduce a variational framework for inference from noisy observations of a MJP. In the second part, I will present some more recent work on hybrid systems where a latent MJP drives the dynamics of continuous observed variables.
Guido Sanguinetti obtained a Laurea (MSc) in Physics from the University of Genova in 1998 and a DPhil in Mathematics from the University of Oxford in 2004. Between 2004 and 2006 he was a RA in the Machine Learning group at the University of Sheffield, working with Neil Lawrence and Magnus Rattray. In 2006, he was appointed to a lectureship in Computer Science. He is now a Lecturer in Systems Biology, holding a joint appointment between the Departments of Computer Science and the Department of Chemical and Process Engineering. His research interests lie on the design and application of statistical inference techniques to solve molecular biology problems.