Participation in INI programmes is by invitation only. Anyone wishing to apply to participate in the associated workshop(s) should use the relevant workshop application form.
Many application areas within the biological sciences require sophisticated statistical techniques in order to deal with problems associated with large datasets, indirect measurements, complex underlying processes or any combination of these three. For example in population genetics, genetic data from unrelated individuals contains information about the biological processes of inheritance which are closely intertwined with the demographic history of the populations from which the individuals were sampled. In many situations Monte Carlo methods offer the best (or even only) approach for analysing such data sets. Even so, many topical problems in the biological sciences now lie either at or even beyond the limit of what is practicable for current Monte Carlo methods on today's computers. Thus there is considerable interest in extending the power and range of applicability of Monte Carlo methods in order to meet the increasing demands of the applied research community.
The development and application of Monte Carlo methods has been an extremely active research area for many years, motivated in part by the computer revolution of the 1980s. However, different research communities have tended to develop their own Monte Carlo methodology with few interactions between them. This programme will bring together researchers from a range of communities and backgrounds in order to meet, interact and share knowledge. The focus of the programme will be on the development and application of novel Monte Carlo methodology appropriate for the many challenging problems arising from the biological sciences.
The programme will cover such methodological areas as Markov Chain Monte Carlo, Sequential Monte Carlo, Variational approaches and Indirect Inference together with their application in Bioinformatics, Population Ecology, Epidemiology, Evolutionary, Population and Statistical Genetics.