Proceedings of PGM'06
(all names are
without diacritics and accents)
Contents
J. Abellan, M. Gomez-Olmedo,
and S. Moral
Some Variations on the PC Algorithm
P. Antal, A. Gezsi, G. Hullam, and A.
Millinghoffer
Learning Complex Bayesian Network Features for
Classification
P. Antal and A. Millinghoffer
Literature Mining using Bayesian Networks
A. Antonucci and M. Zaffalon
Locally specified credal networks
O. Bangso, N. Sondberg-Madsen, and F.
V. Jensen
A Bayesian Network Framework for the
Construction of Virtual Agents with Human-like Behaviour
J. H. Bolt
Loopy Propagation: the Convergence Error in
Markov Networks
J. H. Bolt and L. C. van der Gaag
Preprocessing the MAP Problem
T. Chen and N. L. Zhang
Quartet-Based Learning of Shallow Latent
Variables
B. R. Cobb
Continuous Decision MTE Influence Diagrams
A. Feelders and J. Ivanovs
Discriminative Scoring of Bayesian Network
Classifiers: a Comparative Study
M. Julia Flores, J. A. Gamez, and S.
Moral
The Independency
tree model: a new approach for clustering and factorisation
O. C. H. Francois and P. Leray
Learning the Tree Augmented Naive Bayes
Classifier from incomplete datasets
L. C. van der Gaag, S. Renooij, and
P. L. Geenen
Lattices for Studying Monotonicity of Bayesian
Networks
L. C. van der Gaag and P. R. de Waal
Multi-dimensional Bayesian Network Classifiers
J. A. Gamez, J. L. Mateo, and J. M.
Puerta
Dependency networks based classifiers: learning
models by using independence
J. A. Gamez, R. Rumi, and A. Salmeron
Unsupervised naive Bayes for data clustering
with mixtures of truncated exponentials
M. A. J. van Gerven and F. J. Diez
Selecting Strategies for Infinite-Horizon
Dynamic LIMIDS
M. A. Gomez-Villegas, Paloma Main,
and R. Susi
Sensitivity analysis of extreme inaccuracies in
Gaussian Bayesian Networks
C. Gonzales and N. Jouve
Learning Bayesian Networks Structure using
Markov Networks
P. O. Hoyer, S. Shimizu, and A. J.
Kerminen
Estimation of linear, non-gaussian causal models
in the presence of confounding
latent variables
R. Jurgelenaite and T. Heskes
Symmetric Causal Independence Models for
Classification
J. Kwisthout and G. Tel
Complexity Results for Enhanced Qualitative
Probabilistic Networks
M. Luque and F. J. Diez
Decision analysis with influence diagrams using
Elvira's explanation facilities
I. Martinez, C. Rodriguez, and A.
Salmeron
Dynamic importance sampling in Bayesian networks
using factorisation of probability trees
S. Meganck, S. Maes, P. Leray, and B.
Manderick
Learning Semi-Markovian Causal Models using
Experiments
J. Morton, L. Pachter, A. Shiu, B.
Sturmfels, and O. Wienand
Geometry of rank tests
J. D. Nielsen and M. Jaeger
An Empirical Study of Efficiency and Accuracy of
Probabilistic Graphical Models
S. H. Nielsen and T. D. Nielsen
Adapting Bayes Network Structures to
Non-stationary Domains
K. G. Olesen, O. K. Hejlesen, R.
Dessau, I. Beltoft, and M. Trangeled
Diagnosing Lyme disease - Tailoring patient
specific Bayesian networks for temporal reasoning
D. Ozgur-Unluakin and Taner Bilgic
Predictive Maintenance using Dynamic
Probabilistic Networks
J. M. Pena, R. Nilsson, J.
Bjorkegren, and J. Tegner
Reading Dependencies from the Minimal Undirected
Independence Map of a Graphoid that Satisfies Weak Transitivity
S. Renooij and L. van der Gaag
Evidence and Scenario Sensitivities in
Naive Bayesian Classifiers
A. Reyes, P. Ibarguengoytia, L. E.
Sucar, and E. Morales
Abstraction and Refinement for Solving
Continuous Markov Decision Processes
G. Santafe, J. A. Lozano, and P.
Larranaga
Bayesian Model Averaging of TAN Models for
Clustering
X. Sun, M. J. Druzdzel, and C. Yuan
Dynamic Weighting A* Search-based MAP Algorithm
for Bayesian Networks
P. Simecek
A Short Note on Discrete Representability
of Independence Models
P. Thwaites and J. Smith
Evaluating Causal effects using Chain Event
Graphs
Y. Wang and N. L. Zhang
Severity of Local Maxima for the EM Algorithm:
Experiences with Hierarchical Latent Class Models
Y. Xiang
Optimal Design with Design Networks
C. Yuan and M. J. Druzdzel
Hybrid Loopy Belief Propagation
A. Zagorecki and M. J. Druzdzel
Probabilistic Independence of Causal Influences