| Graphical Models
|
Graphical models are a graphical representation of the conditional
independence relations among a set of variables. The graph is useful
both as an intuitive representation of how the variables are
related, and as a tool for defining efficient message passing
algorithms for probabilistic inference.
Some relevant publications:
- Silva, R. Chu, W. and Ghahramani, Z. (2008). Hidden common cause
relations in relational learning. In Advances on Neural Information Processing
Systems (NIPS-2007).
- Zhang, J., Ghahramani, Z, and Yang, Y. (2008) Flexible latent
variable models for multi-task learning. Machine Learning.
- Pérez-Cruz, F. Ghahramani, Z. and Pontil, M. (2007) .
Conditional
Graphical Models.
In Predicting Structured Data, Edited by
G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar and
S. V. N. Vishwanathan, MIT Press, September.
- Silva, R. and Ghahramani, Z. (2006)
Bayesian Inference for Gaussian Mixed Graph
Models.
In Uncertainty in Artificial Intelligence
(UAI-2006).
- Wood, F., Griffiths, T.L. and Ghahramani, Z. (2006)
A Non-Parametric Bayesian Method for
Inferring Hidden Causes.
In Uncertainty in Artificial
Intelligence (UAI-2006) pp. 536-543.
- Beal, M.J. and Ghahramani, Z. (2006)
Variational Bayesian
learning of directed graphical models with hidden variables.
Bayesian Analysis 1:793--832.
- Chu, W. Ghahramani, Z. and Wild D.L. (2004)
A Graphical Model for Protein Secondary
Structure Prediction.
In Proceedings of the Twenty-First
International Conference on Machine Learning
(ICML-2004). Morgan-Kaufmann, pp. 161-168.
- Murray, I. and Ghahramani, Z. (2004)
Bayesian Learning in
Undirected Graphical Models: Approximate MCMC
algorithms.
In
Uncertainty in Artificial Intelligence
(UAI-2004).
- Ghahramani, Z. (2002)
Graphical models: parameter
learning.
In Arbib, M. A. (ed.) Handbook
of Brain Theory and Neural Networks, Second Edition. MIT Press.
-
Beal, M. J. and Ghahramani, Z. (2002)
The Variational Bayesian EM Algorithm for Incomplete Data: with
Application to Scoring Graphical Model Structures [pdf]
[abstract]
In Bayesian Statistics 7
- Ghahramani, Z. and Beal, M.J. (2001)
Propagation algorithms for
variational Bayesian learning [ps] [pdf] [abstract]
In Leen, T.K., Dietterich, T.G., and Tresp, V. (eds)
Neural Information Processing Systems 13:507-513. MIT
Press.
- Ghahramani, Z. (2001)
An Introduction to Hidden Markov Models and Bayesian Networks [ps] [pdf]
International Journal of Pattern Recognition and
Artificial Intelligence 15(1):9-42.
- Ghahramani, Z. and
Beal, M.J. (2000)
Graphical models and variational methods
In Saad & Opper (eds)
Advanced Mean Field Method---Theory and Practice. MIT
Press
-
Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. (1999)
An introduction to variational methods for graphical models
Machine Learning 37:183-233.