| On the Acceptability of Arguments in Preference-based Argumentation (p. 1) |
| by Amgoud, L |
| Merging uncertain knowledge bases in a possibilistic logic framework (p. 8) |
| by Benferhat, S |
| A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity (p. 16) |
| by Bloemeke, M |
| Structured Reachability Analysis for Markov Decision Processes (p. 24) |
| by Boutilier, C |
| Tractable Inference for Complex Stochastic Processes (p. 33) |
| by Boyen, X |
| Empirical Analysis of Predictive Algorithms for Collaborative Filtering (p. 43) |
| by Breese, J S |
| Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus (p. 53) |
| by De Campos, L M |
| Dealing with Uncertainty in Situation Assessment : towards a Symbolic Approach(p. 61) |
| by Castel, C |
| Marginalizing in Undirected Graph and Hypergraph Models (p. 69) |
| by Castillo, E F |
| Utility Elicitation as a Classification Problem (p. 79) |
| by Chajewska, U S |
| Irrelevance and Independence Relations in Quasi-Bayesian Networks (p. 89) |
| by Cozman, F |
| Dynamic Jointrees (p. 97) |
| by Darwiche, A |
| On the Semi-Markov Equivalence of Causal Models (p. 105) |
| by Desjardins, B |
| Comparative uncertainty, belief functions and accepted beliefs (p. 113) |
| by Dubois, D |
| Qualitative Decision Theory with Sugeno Integrals (p. 121) |
| by Dubois, D |
| The Bayesian Structural EM Algorithm (p. 129) |
| by Friedman, N |
| Learning the Structure of Dynamic Probabilistic Networks (p. 139) |
| by Friedman, N |
| Learning by Transduction (p. 148) |
| by Gammerman, A |
| Graphical Models and Exponential Families (p. 156) |
| by Geiger, D |
| Psychological and Normative Theories of Causal Power and the Probabilities of Causes (p. 166) |
| by Glymour, C |
| Updating Sets of Probabilities (p. 173) |
| by Grove, A J |
| Minimum Encoding Approaches for Predictive Modeling (p. 183) |
| by Gruenwald, P |
| Toward Case-Based Preference Elicitation : Similarity Measures on Preference Structures(p. 193) |
| by Ha, V |
| Axiomatizing Causal Reasoning (p. 202) |
| by Halpern, J Y |
| Solving POMDPs by Searching in Policy Space (p. 211) |
| by Hansen, E A |
| Hierarchical solution of Markov decision processes using macro-actions (p. 220) |
| by Hauskrecht, M |
| Inferring Informational Goals from Free-Text Queries : A Bayesian Approach(p. 230) |
| by Heckerman, D |
| Evaluating Las Vegas Algorithms-Pitfalls and Remedies (p. 238) |
| by Hoos, H H |
| An Anytime Algorithm for Decision Making under Uncertainty (p. 246) |
| by Horsch, M C |
| The Lumiere Project : Bayesian User Modeling for Inferring the Goals and Needs of Software Users(p. 256) |
| by Horvitz, E |
| Any Time Probabilistic Reasoning for Sensor Validation (p. 266) |
| by Ibargueengoytia, P H |
| Measure Selection : Notions of Rationality and Representation Independence(p. 274) |
| by Jäger, M |
| Implementing Resolute Choice Under Uncertainty (p. 282) |
| by Jaffray, J Y |
| Dealing with uncertainty on the initial state of a Petri net (p. 289) |
| by Jarkass, I |
| Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions : A Survey of Approximation and Consistency Results(p. 296) |
| by Jiang, W |
| Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks (p. 304) |
| by Kearns, M |
| Large Deviation Methods for Approximate Probabilistic Inference (p. 311) |
| by Kearns, M |
| Mixture Representations for Inference and Learning in Boltzmann Machines (p. 320) |
| by Lawrence, N D |
| A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions (p. 328) |
| by Lepar, V |
| Incremental Tradeoff Resolution in Qualitative Probabilistic Networks (p. 338) |
| by Liu, C L |
| Using Qualitative Relationships for Bounding Probability Distributions (p. 346) |
| by Liu, C L |
| Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge (p. 354) |
| by Lukasiewicz, T |
| Lazy Propagation in Junction Trees (p. 362) |
| by Madsen, A L |
| Constructing Situation Specific Belief Networks (p. 370) |
| by Mahoney, S M |
| Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data (Invited Paper (p. 379) |
| by Manski, C F |
| An Experimental Comparison of Several Clustering and Initialization Methods (p. 386) |
| by Meila, M |
| From Likelihood to Plausibility (p. 396) |
| by Monney, P A |
| A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data (p. 404) |
| by Monti, S |
| Resolving Conflicting Arguments under Uncertainties (p. 414) |
| by Ng, B H K |
| Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems (p. 422) |
| by Parr, R |
| Logarithmic Tune Parallel Bayesian Inference (p. 431) |
| by Pennock, D M |
| Learning From What You Don't Observe (p. 439) |
| by Peot, M A |
| Context-specific approximation in probabilistic inference (p. 447) |
| by Poole, D |
| Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding (p. 455) |
| by Rish, I |
| Decision Theoretic Foundations of Graphical Model Selection (p. 464) |
| by Sebastiani, P |
| On the Geometry of Bayesian Graphical Models with Hidden Variables (p. 472) |
| by Settimi, R |
| Bayes-Ball : The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams(p. 480) |
| by Shachter, R D |
| Switching Portfolios (p. 488) |
| by Singer, Y |
| Bayesian Networks from the Point of View of Chain Graphs (p. 496) |
| by Studeny, M |
| Learning Mixtures of DAG Models (p. 504) |
| by Thiesson, B |
| Probabilistic Inference in Influence Diagrams (p. 514) |
| by Zhang, N L |
| Planning with Partially Observable Markov Decision Processes : Advances in Exact Solution Method(p. 523) |
| by Zhang, N L |
| Flexible and Approximate Computation through State-Space Reduction (p. 531) |
| by Zhang, W |