Uncertainty in Artificial Intelligence
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Prakash P. Shenoy
School of Business
University of Ka
Contact Information
Webpage : http://lark.cc.ku.edu/~pshenoy
Email : pshenoy@ku.edu
Phone : (785) 864-
Fax : (785) 864-
Mailing Address
1300 Sunnyside Ave., 313B Summerfield Hall
Lawrence,  KS  66045-7585
USA
Publications in UAI
UAI-1990
:: Valuation-based Systems for Discrete Optimization
UAI-1988
:: Axioms for Probability and Belief-Function Propagation
UAI-1987
:: Modifiable Combining Functions
UAI-1986
:: Propagation of Belief Functions: A Distributed Approach
UAI-2010
:: Solving Hybrid Influence Diagrams with Deterministic Variables
UAI-2006
:: Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians
UAI-2005
:: Hybrid Bayesian Networks with Linear Deterministic Variables
UAI-2004
:: Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
UAI-2003
:: A Linear Belief Function Approach to Portfolio Evaluation
:: Decision Making with Partially Consonant Belief Functions
UAI-2002
:: Statistical Decisions Using Likelihood Information Without Prior Probabilities
UAI-2001
:: A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
UAI-2000
:: A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
UAI-1999
:: On Transformations between Probability and Spohnian Disbelief Functions
UAI-1998
:: A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions
UAI-1996
:: Binary Join Trees
UAI-1995
:: A New Pruning Method for Solving Decision Trees and Game Trees
UAI-1993
:: Valuation Networks and Conditional Independence
UAI-1992
:: Conditional Independence in Uncertainty Theories
UAI-1991
:: A Fusion Algorithm for Solving Bayesian Decision Problems
UAI-1990
:: Valuation-Based Systems for Discrete Optimization
UAI-1988
:: An Axiomatic Framework for Bayesian and Belief-function Propagation
UAI-1987
:: Modifiable Combining Functions
UAI-1986
:: Propagation of Belief Functions: A Distributed Approach


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