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
:: Decision Making with Partially Consonant Belief Functions
:: A Linear Belief Function Approach to Portfolio Evaluation

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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