Uncertainty in Artificial Intelligence
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Dan Geiger
Department of Computer Science
Technion-Israel
Contact Information
Email : dang@cs.technion.ac.il
Mailing Address
Haifa,  32000
Israel
Publications in UAI
UAI-1990
:: Separable and Transitive Graphoids
UAI-1989
:: d-Separation: From Theorems to Algorithms
UAI-1988
:: On the Logic of Causal Models
UAI-2007
:: Importance Sampling via Variational Optimization
UAI-2003
:: A Distance-Based Branch and Bound Feature Selection Algorithm
:: Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models
UAI-2002
:: Factorization of Discrete Probability Distributions
:: Asymptotic Model Selection for Naive Bayesian Networks
UAI-2000
:: Perfect Tree-Like Markovian Distributions
:: Likelihood Computations Using Value Abstractions
UAI-1999
:: Quantifier Elimination for Statistical Problems
:: Random Algorithms for the Loop Cutset Problem
:: Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
UAI-1998
:: Graphical Models and Exponential Families
UAI-1996
:: Asymptotic Model Selection for Directed Networks with Hidden Variables
:: A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees
UAI-1995
:: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
:: Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
UAI-1994
:: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
:: Approximation Algorithms for the Loop Cutset Problem
:: Learning Gaussian Networks
:: On Testing Whether an Embedded Bayesian Network Represents a Probability Model
UAI-1993
:: Inference Algorithms for Similarity Networks
UAI-1992
:: An Entropy-based Learning Algorithm of Bayesian Conditional Trees
UAI-1991
:: Advances in Probabilistic Reasoning
UAI-1990
:: Practical and Theoretical Advances in Knowledge Acquisition of Probabilistic Networks
UAI-1989
:: d-Separation: From Theorems to Algorithms
UAI-1988
:: On the Logic of Causal Models


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