Wang, S.S.J. and Wand, M. P., Using Infer.NET for Statistical Analyses, Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 06-10, 2010, 16p.
We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family of deterministic approximation algorithms rather than Markov chain Monte Carlo. The underlying rationale is that such deterministic algorithms can handle bigger problems due to their increased speed, despite some loss of accuracy. We find that Infer.NET is a well-designed computational framework with intuitive syntax. Nevertheless, the current release is limited in terms of the breadth of models it can handle, and speed of execution on standard hardware platforms.