Theme E: Highly structured stochastic systems

Theme Manager: Dr M.A. Hurn, Mathematical Sciences

OVERVIEW

• The development, analysis and testing of a stochastic model for comparative phylogenetic processes.
• Pathogenetic population modelling and sensitivity analysis.
• Inclusion of stochastic terms into computational algorithms for porous medium simulation codes and the statistical analysis of the resulting flow realisations.
• Bayesian modelling in large scale spatial/temporal applications (ionospheric mapping and pollution modelling).
• Liaison with other themes on the stochastic analysis of random high contrast media and on random networks.
• A major international workshop on Complex Stochastic Systems

CURRENT RESEARCH HIGHLIGHTS

Theme E has a slightly unusual role, bringing together stochastic ideas and applications in other themes as well as tackling several statistical complex systems of our own. Besides a steady stream of visits and visitors, one of the activities of which we've been most proud is the spread of workshops we've organised, bringing together people from Bath and beyond in a number of different fields. As well as the very enjoyable Theme E conference in September 2006, we also held a joint LMS meeting with Theme A on "Analysis and stochastics of growth processes" that month. This was followed by a research kitchen on "Approximate Inference" in May 2007 mixing statisticians and the machine learning and engineering communities. July 2007 brought the first of our two mini-workshops on "Modelling complex environmental spatial and temporal data". Finally this July we built on our biological links with Theme B in hosting a Postgraduate Workshop "Probabilistic Techniques in Population Genetics". Some happy delegates from the latest of these meetings can be seen in the picture below.

Professor Albert Shiryaev, renowned Russian Mathematician and member of Russian Academy of Sciences, toasting the anniversary of the publication of Kolmogorov's book "Foundations of the Theory of Probability" which gives the first axiomatic treatment of probability through measure theory thus giving birth to modern probability theory as we know it today.

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