A comparison of algorithms for inference and learning in probabilistic graphical models (2005)

by Brendan J. Frey , Nebojsa Jojic
Venue:IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:49 - 4 self

Active Bibliography

11 Advances in Algorithms for Inference and Learning in Complex Probability Models for Vision – Brendan J. Frey, Nebojsa Jojic - 2002
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
Proposed design for gR, a graphical models toolkit for R – Kevin P. Murphy - 2003
18 Unsupervised learning – Zoubin Ghahramani - 2004
2 Graphical Models for Statistical Inference and Data Assimilation – Alexander T. Ihler, Sergey Kirshner, Michael Ghil, Andrew W. Robertson, Padhraic Smyth - 2005
Graphical Models for Biclustering . . . – José Caldas - 2012
1 Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models – Yee Whye Teh, Yee Whye Teh - 2003
2 Graphical Models: Parameter Learning – Zoubin Ghahramani - 2003
7 Prior Information and Generalized Questions – Jörg C. Lemm - 1996
Bayesian Modelling in Machine Learning: A Tutorial Review – Matthias Seeger - 2009
9 Exploiting parameter domain knowledge for learning in Bayesian networks – Radu Stefan Niculescu - 2005
82 Computer Vision: Algorithms and Applications – Richard Szeliski - 2010
176 The Bayes Net Toolbox for MATLAB – Kevin P. Murphy - 2001
11 Learning static object segmentation from motion segmentation – Michael Gregory Ross - 2005
Generative Models for 2-D images of 3-D scenes – Anitha Kannan - 2007
8 An Introduction to Variational Methods for Graphical Methods – Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul - 1998
Embedded trees: Estimation of Gaussian . . . – Erik B. Sudderth - 2002
31 An Introduction to Graphical Models – Kevin P. Murphy - 2001
Machine Learning in Computational Biology: Models of Alternative Splicing – Ofer Shai - 2009