- Preface
- About PGM'02
- Optimal Time-Space Tradeoff
in Probabilistic Inference
David Allen and Adnan Darwiche
- Floating Search Methods in Learning Bayesian
Networks
Rosa Blanco, Iñaki Inza, and Pedro Larrañaga
- Learning Essential Graph Markov Models From
Data
Robert Castelo and Michael D. Perlman
- Multicausal Prior Families, Randomisation and Essential
Graphs
Alireza Daneshkhah and Jim. Q. Smith
- Reducing Propagation Effort in Large Polytrees: An Application to
Information Retrieval
Luis M. de Campos, Juan M. Fernández-Luna, and Juan F. Huete
- Graphical Models to Causal Discovery from
Data
Luis M. de Campos, José A. Gámez, and José M. Puerta
- Continuous Speech Recognition Using Dynamic Bayesian Networks. A
Fast Decoding Algorithm
Murat Deviren and Khalid Daoudi
- New Structures for Conditional Probability
Tables
J.A. Fernández. del Pozo and C. Bieza
- A Practical Relaxation of Constant-Factor Treewidth Approximation
Algorithms
Mark Hopkins and Adnan Darwiche
- Probabilistic Decision Graphs - Combining Verification and
AI Techniques for Probabilistic Inference
Manfred Jaeger
- Scaled Conjugate Gradients for Maximum Likelihood: An Empirical
Comparison with the EM Algorithm
Kristian Kersting and Niels Landwehr
- Logical Hidden Markov
Models (Extendes abstract)
K. Kersting, T. Raiko, and L. De Raedt
- Causal Models, Value of Intervention, and
Search for Opportunities
Tsai-Ching Lu, Marek J. Druzdzel
- Restricted Bayesian Network Structure
Learning
Peter Lucas
- Factorisation of Probability Trees and its Application to Inference
in Bayesian Networks
Irene Martínez, Serafín Moral, Carmelo Rodríguez, and Antonio Salmerón
- Estimating Mixtures of Truncated Exponentials from
Data
Serafín Moral, Rafael Rumí, and Antonio Salmerón
- Unsupervised Learning of Bayesian Networks Via Estimation
of Distribution Algorithms
J.M. Peña, J.A. Lozano, and P. Larrañaga
- Inferentially Efficient Propagation in Non-Decomposable
Bayesian Network with Hierarchical Junction Trees
Roberto O. Puch, Jim Q. Smith, and Concha Bielza
- Characterization of Essential Graphs by Means of an Operation
of Legal Component Merging
Milan Studený
- Approximate Solutions of Complex Influence Diagrams through
MCMC Methods.
M.A. Virto, J. Martín, D. Ríos-Insua, and A. Moreno
- Bayesian Networks
in Educational Testing
Jirí Vomlel
- An Extension of Lazy Evaluation for Influence Diagrams Avoiding
Redundant Variables in the Potentials
Marta Vomlelová and Finn V. Jensen
- Cooperative Verification
of Agent Interface
Y. Xiang and X. Chen
- Handling of Deterministic Relationships in Constraint-based
Causal Discovery
Yusuf Kenan Yilmaz, Ethem Alpaydin, Levent Akin, and Taner Bilgiç
- Hugin - The Tool for Bayesian Networks and Influence
Diagrams
Frank Jensen, Uffe B. Kjærulff, Michael Lang, and Anders L. Madsen
- Elvira: An Environment for Creating and Using Probabilistic Graphical
Models.
Elvira Consortium
