Infer.NET - Practical Implementation Issues and a Comparison of Approximation Techniques
published: Dec. 31, 2007, recorded: December 2007, views: 9955
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Infer.NET is an efficient, general-purpose inference engine developed at Microsoft Cambridge by Tom Minka, John Winn and others. It aims to be highly efficient, general purpose and extensible --- three normally contradictory goals. We have largely managed to achieve these goals using a compiler-like architecture, so that code is generated to perform the desired inference task. Infer.NET can apply one of a range of inference algorithms to a given probabilistic model, and so provides a useful framework for comparing the performance of different algorithms. In this talk, I will describe the capabilities and infrastructure of Infer.NET and give examples of applying both expectation propagation and variational message passing on the same model. I will also describe some failure cases that we have encountered for each algorithm.
Download slides: abi07_winn_ipi_01.pdf (326.7 KB)
Download slides: abi07_winn_ipi_01.ppt (1.0 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !