@misc{minka2009automating,
author = {Minka, Tom},
title = {Automating Variational Inference for Statistics and Data Mining},
year = {2009},
month = {August},
abstract = {I will describe Infer.NET, a free software package from Microsoft that automatically applies variational Bayesian inference to a statistical model of your choosing. Unlike sampling methods, variational methods approximate the posterior distribution as a point estimate plus uncertainty, making them well suited to large-scale time-varying datasets. Infer.NET is structured as a compiler: it takes a model specification as input and produces a specialized inference program as output. This automated process makes it easy to experiment with different models and get an efficient program for each one. I will demonstrate Infer.NET with models from the psychometric literature.},
url = {http://approjects.co.za/?big=en-us/research/publication/automating-variational-inference-statistics-data-mining/},
edition = {Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 2009)},
note = {Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 2009)},
}