%0 Journal Article %1 FoxRoberts12air %A Fox, Charles %A Roberts, Stephen %D 2012 %J Artificial Intelligence Review %K v1500 springer paper ai knowledge processing pattern learn algorithm %N 2 %P 85-95 %R 10.1007/s10462-011-9236-8 %T A Tutorial on Variational Bayesian Inference %V 38 %X This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense. It then derives local node updates and reviews the recent Variational Message Passing framework.

@article{FoxRoberts12air, abstract = {This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the {KL-divergence} sense. It then derives local node updates and reviews the recent Variational Message Passing framework.}, added-at = {2012-08-09T11:15:36.000+0200}, author = {Fox, Charles and Roberts, Stephen}, biburl = {https://www.bibsonomy.org/bibtex/2b4adad10fbca59ee4224652b06137c69/flint63}, doi = {10.1007/s10462-011-9236-8}, file = {SpringerLink:2012/FoxRoberts12air.pdf:PDF}, groups = {public}, interhash = {0dad87b6a87ed7dd7bbc8b566f705790}, intrahash = {b4adad10fbca59ee4224652b06137c69}, issn = {0269-2821}, journal = {Artificial Intelligence Review}, keywords = {v1500 springer paper ai knowledge processing pattern learn algorithm}, number = 2, pages = {85-95}, timestamp = {2018-04-16T12:30:46.000+0200}, title = {A Tutorial on Variational {Bayesian} Inference}, username = {flint63}, volume = 38, year = 2012 }