Active Bibliography

564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
124 Learning dynamic Bayesian networks – Zoubin Ghahramani - 1998
11 An EM algorithm for identification of nonlinear dynamical systems – Sam Roweis, Zoubin Ghahramani - 2000
A hybrid approach of physical laws and data-driven modeling for estimation: the example of . . . – Aude Hofleitner - 2013
Continuous-state Graphical Models for . . . – Leonid Sigal - 2008
5 Technical Introduction: A Primer on Probabilistic Inference – Thomas L. Griffiths, Alan Yuille - 2006
Bayesian Modelling in Machine Learning: A Tutorial Review – Matthias Seeger - 2009
Probabilistic Variational Methods for Vision based Complex Motion Analysis – Gang Hua
5 Machine Learning for Computer Graphics: A Manifesto and Tutorial – Aaron Hertzmann - 2003
11 Learning Deep Generative Models – Ruslan Salakhutdinov - 2009
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Learning the Dynamics of Arterial Traffic From Probe – Aude Hofleitner, Ryan Herring, Pieter Abbeel, Re Bayen
Proposed design for gR, a graphical models toolkit for R – Kevin P. Murphy - 2003
2 Machine Learning: A Probabilistic Approach – David Barber - 2006
9 Exploiting parameter domain knowledge for learning in Bayesian networks – Radu Stefan Niculescu - 2005
2 Learning to Parse Images – Yee Whye Teh - 2000
2 Graphical Models: Parameter Learning – Zoubin Ghahramani - 2003
49 A comparison of algorithms for inference and learning in probabilistic graphical models – Brendan J. Frey, Nebojsa Jojic - 2005
1 INTERACTION BETWEEN MODULES IN LEARNING SYSTEMS FOR VISION APPLICATIONS BY – Amit Sethi
11 Advances in Algorithms for Inference and Learning in Complex Probability Models for Vision – Brendan J. Frey, Nebojsa Jojic - 2002