 CARMA COLLOQUIUM
 Speaker: Brandon Turner, Stanford University
 Title: ABCDE: A practical likelihoodfree Bayesian analysis technique with applications to mathematical models of cognition
 Location: Room V206, Mathematics Building (Callaghan Campus) The University of Newcastle
 Time and Date: 4:00 pm, Wed, 24^{th} Oct 2012
 Abstract:
Many cognitive models derive their predictions through simulation. This means that it is difficult or impossible to write down a probability distribution or likelihood that characterizes the random behavior of the data as a function of the model's parameters. In turn, the lack of a likelihood means that standard Bayesian analyses of such models are impossible. In this presentation we demonstrate a procedure called approximate Bayesian computation (ABC), a method for Bayesian analysis that circumvents the evaluation of the likelihood. Although they have shown great promise for likelihoodfree inference, current ABC methods suffer from two problems that have largely revented their mainstream adoption: long computation time and an inability to scale beyond models with few parameters. We introduce a new ABC algorithm, called ABCDE, that includes differential evolution as a computationally efficient genetic algorithm for proposal generation. ABCDE is able to obtain accurate posterior estimates an order of magnitude faster than a popular rejectionbased method and scale to highdimensional parameter spaces that have proven difficult for the current rejectionbased ABC methods. To illustrate its utility we apply ABCDE to several wellestablished simulationbased models of memory and decisionmaking that have never been fit in a Bayesian framework.
AUTHORS: Brandon M. Turner (Stanford University) Per B. Sederberg (The Ohio State University)
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