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Evolution is a complex process that involves many sources of variation
and interactions that make mathematical modeling a challenge. In nature, evolution is a time dependent
process that involves a large number of environmental variables influencing the adaptation of a
population and its progress. Environmental effects include the interaction between a population
with its physical environment, its interaction with other populations and species, and the within
population interactions between its members. Accounting for all of these sources of variation in a
mathematical model is a challenge. Experimental evolution is no exception, although in such a process
the effect of the physical environment is controlled and interactions with other species are limited.
Nevertheless, statistical inference of the forces of evolution underlying the process of adaptation in
an experimental evolution can be computationally expensive and can involve complicated model modeling.
In this presentation we propose an Approximate Bayesian Computation (ABC) approach to simplify inference
in experimental evolution. The use of this approach can be extended to other areas of inference when the
underlying models of interest are complex and hard to fit using maximum likelihood or regular Bayesian
approaches.
All interested faculty, staff, and graduate students are invited to attend.
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