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Individual Based Models (IBMs) and Agent Based Models (ABMs) have becomewidely used tools to understand complex biological systems. However, generalmethods of parameter inference for IBMs are not available. In this paper weshow that it is possible to address this problem with a traditionallikelihood-based approach, using an example of an IBM developed to describe thespread of Chytridiomycosis in a population of frogs as a case study. We showthat if the IBM satisfies certain criteria we can find the likelihood (orposterior) analytically, and use standard computational techniques, such asMarkov Chain Monte Carlo (MCMC), for parameter inference.

Parameter Inference for an Individual Based Model of Chytridiomycosis in Frogs