Active Bibliography

393 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
101 Learning dynamic Bayesian networks – Zoubin Ghahramani - 1998
9 An EM algorithm for identification of nonlinear dynamical systems – Sam Roweis, Zoubin Ghahramani - 2000
Continuous-state Graphical Models for . . . – Leonid Sigal - 2008
6 Exploiting parameter domain knowledge for learning in Bayesian networks – Radu Stefan Niculescu - 2005
11 Advances in Algorithms for Inference and Learning in Complex Probability Models for Vision – Brendan J. Frey, Nebojsa Jojic - 2002
83 Multiresolution markov models for signal and image processing – Alan S. Willsky - 2002
37 Switching State-Space Models – Zoubin Ghahramani, Geoffrey E. Hinton - 1996
27 Graphical Models and Variational Methods – Zoubin Ghahramani, Matthew J. Beal, London England - 2001
4 Technical Introduction: A Primer on Probabilistic Inference – Thomas L. Griffiths, Alan Yuille - 2006
Probabilistic Variational Methods for Vision based Complex Motion Analysis – Gang Hua
53 Tutorial on Variational Approximation Methods – Tommi S. Jaakkola - 2000
4 Machine Learning for Computer Graphics: A Manifesto and Tutorial – Aaron Hertzmann - 2003
5 Learning Deep Generative Models – Ruslan Salakhutdinov - 2009
10 Linear Gaussian models for speech recognition – Antti-Veikko Ilmari Rosti - 2004
2 Variational inference for continuous sigmoidal Bayesian networks – Brendan J. Frey - 1996
Proposed design for gR, a graphical models toolkit for R – Kevin P. Murphy - 2003
An Auxiliary Variational Method – Felix Agakov David, David Barber
5 Reinforcement learning for factored markov decision processes – Brian Sallans - 2002