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D-Index & Metrics

Computer Science

D-Index
40
Citations
7901
World Ranking
9208
National Ranking
3918

Overview

Prakash P. Shenoy is affiliated with the University of Kansas in the United States and has contributed extensively to research in computer science, decision sciences, and mathematics. Their scholarly work primarily focuses on artificial intelligence, statistics and probability, and management science and operations research, along with specific interests in general decision sciences and computer vision and pattern recognition.

Their research corpus includes 22 publications in computer science, 8 in decision sciences, and 8 in mathematics. Subfields of particular focus are artificial intelligence with 20 publications, statistics and probability with 8, management science and operations research with 7, and contributions in general decision sciences and computer vision and pattern recognition.

Their main research topics cover Bayesian modeling and causal inference, multi-criteria decision making, logic, reasoning and knowledge, fuzzy systems and optimization, statistical methods and inference, advanced statistical methods and models, and machine learning applications in healthcare.

Research venues where Prakash P. Shenoy has frequently published include:

  • International Journal of Approximate Reasoning
  • SSRN Electronic Journal
  • INFORMS Journal on Computing
  • Journal of Advanced Zoology

Notable recent papers authored or co-authored by Prakash P. Shenoy include:

  • On properties of a new decomposable entropy of Dempster-Shafer belief functions, 2020, International Journal of Approximate Reasoning
  • An interval-valued utility theory for decision making with Dempster-Shafer belief functions, 2020, International Journal of Approximate Reasoning
  • Entropy for evaluation of Dempster-Shafer belief function models, 2022, International Journal of Approximate Reasoning
  • Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach, 2023, International Journal of Approximate Reasoning
  • On conditional belief functions in directed graphical models in the Dempster-Shafer theory, 2023, International Journal of Approximate Reasoning

Frequent collaborators include Radim Jiroušek, Václav Kratochvíl, Yi Tan, Ben Sherwood, and Thierry Denœux. These coauthors have worked together on multiple papers contributing to the field of approximate reasoning and belief function theory.

Best Publications

  • Axioms for probability and belief-function propagation

    Prakash P. Shenoy;Glenn Shafer

  • A causal mapping approach to constructing Bayesian networks

    Sucheta Nadkarni;Prakash P. Shenoy

  • Valuation-based systems for Bayesian decision analysis

    Prakash P. Shenoy

  • Propagating Belief Functions with Local Computations

    Prakash P. Shenoy;Glenn Shafer

  • A Bayesian network approach to making inferences in causal maps

    Sucheta Nadkarni;Prakash P Shenoy

  • Propagating belief functions in qualitative Markov trees

    Glenn Shafer;Prakash P. Shenoy;Khaled Mellouli

  • Probability propagation

    Unknown

  • On coalition formation: a game-theoretical approach

    Prakash P. Shenoy

  • On the plausibility transformation method for translating belief function models to probability models

    Barry R. Cobb;Prakash P. Shenoy

  • A valuation-based language for expert systems

    P. P. Shenoy

  • Using Bayesian networks for bankruptcy prediction: Some methodological issues

    Lili Sun;Prakash P. Shenoy

  • Valuation-based systems: a framework for managing uncertainty in expert systems

    Prakash P. Shenoy

  • Binary Join Trees for Computing Marginals in the Shenoy-Shafer Architecture

    Prakash P. Shenoy

  • Conditional independence in valuation-based systems

    Prakash P. Shenoy

  • A new definition of entropy of belief functions in the Dempster–Shafer theory

    Radim Jiroušek;Prakash P. Shenoy

  • Inference in hybrid Bayesian networks using mixtures of polynomials

    Prakash P. Shenoy;James C. West

  • Local Computation in Hypertrees

    Glenn R. Shafer;Prakash P. Shenoy

  • A Comparison of Bayesian and Belief Function Reasoning

    Barry R. Cobb;Prakash P. Shenoy

  • Inference in hybrid Bayesian networks with mixtures of truncated exponentials

    Barry R. Cobb;Prakash P. Shenoy

  • On Spohn's rule for revision of beliefs

    Prakash P. Shenoy

  • Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

    Barry R. Cobb;Prakash P. Shenoy;Rafael Rumí

  • Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence

    Dan Geiger;Prakash Pundalik Shenoy

Frequent Co-Authors

Concha Bielza
Concha Bielza Technical University of Madrid
Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Dan Geiger
Dan Geiger Technion – Israel Institute of Technology
Finn Verner Jensen
Finn Verner Jensen Aalborg University
Eric Horvitz
Eric Horvitz Microsoft (United States)
Gautam Biswas
Gautam Biswas Vanderbilt University

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