Probabilistic modeling and Bayesian inference

Probabilistic generative models provide a powerful framework for analyzing and thinking about complex, noisy systems. When modeling the underlying process that generated the data set of interest, many problems can be framed as an inversion of this process, using Bayesian inference. To get started with probabilistic modeling, Michael Lee and E.-J. Wagenmakers have written a fantastic book on Bayesian Graphical Modeling (available freely in PDF form), with detailed examples coded in WinBUGS, a modern implementation of BUGS. The book's examples are geared for cognitive scientists, though the kinds of models presented are widely applicable. For general reading on Bayesian inference, check out Tom Griffiths' Reading list on Bayesian methods.


This page contains some information on probabilistic modeling with the BUGS language.