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- Prakash P. Shenoy

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
35
Citations
6,800
130
World Ranking
7515
National Ranking
3534

- Artificial intelligence
- Statistics
- Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Bayesian network, Theoretical computer science, Discrete mathematics and Machine learning. His Artificial intelligence research integrates issues from Transformation, Valuation and Missing data. His Bayesian network study combines topics in areas such as Structure, Domain knowledge and Bayesian probability.

His research in Theoretical computer science intersects with topics in Dempster–Shafer theory, Computation and Probability distribution. His work carried out in the field of Discrete mathematics brings together such families of science as Factorization, Mathematical economics, Random binary tree, Conditional independence and Axiom. The various areas that Prakash P. Shenoy examines in his Machine learning study include Applied mathematics, Probability density function, Inference and Exponential function.

- Axioms for probability and belief-function propagation (505 citations)
- A causal mapping approach to constructing Bayesian networks (227 citations)
- Valuation-based systems for Bayesian decision analysis (222 citations)

Prakash P. Shenoy mostly deals with Artificial intelligence, Bayesian network, Mathematical economics, Theoretical computer science and Mathematical optimization. The concepts of his Artificial intelligence study are interwoven with issues in Transformation, Valuation and Machine learning. His Bayesian network research is multidisciplinary, incorporating perspectives in Probability density function, Inference, Heuristic, Applied mathematics and Conditional probability distribution.

His Mathematical economics research is multidisciplinary, relying on both Dempster–Shafer theory, Axiom, Axiomatic system, Lottery and Decision theory. His work focuses on many connections between Theoretical computer science and other disciplines, such as Probabilistic logic, that overlap with his field of interest in Representation theorem. His Mathematical optimization study incorporates themes from Valuation, Influence diagram, Decision problem and Joint probability distribution.

- Artificial intelligence (30.86%)
- Bayesian network (23.46%)
- Mathematical economics (20.99%)

- Theoretical computer science (19.14%)
- Bayesian network (23.46%)
- Mathematical economics (20.99%)

His scientific interests lie mostly in Theoretical computer science, Bayesian network, Mathematical economics, Inference and Dempster–Shafer theory. Prakash P. Shenoy interconnects Probability theory, Decision tree, Representation, Binary number and Generalization in the investigation of issues within Theoretical computer science. His Bayesian network study combines topics from a wide range of disciplines, such as Probability density function, Hypercube, Heuristic, Applied mathematics and Algorithm.

His work carried out in the field of Mathematical economics brings together such families of science as Probabilistic logic, Axiom, Axiomatic system and Decision theory. Prakash P. Shenoy interconnects Factorization and Function in the investigation of issues within Axiom. His Inference study is concerned with the field of Artificial intelligence as a whole.

- Inference in hybrid Bayesian networks using mixtures of polynomials (92 citations)
- A new definition of entropy of belief functions in the Dempster–Shafer theory (64 citations)
- Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks (25 citations)

- Statistics
- Artificial intelligence
- Machine learning

His primary areas of investigation include Bayesian network, Inference, Dempster–Shafer theory, Theoretical computer science and Artificial intelligence. His research integrates issues of Hypercube, Conditional probability, Probability density function and Data mining in his study of Bayesian network. His Inference research incorporates elements of Class and Exponential function.

His Dempster–Shafer theory research is multidisciplinary, relying on both Entropy, Mathematical economics, Decision theory and Algorithm. His Theoretical computer science study frequently draws connections between related disciplines such as Influence diagram. His research ties Machine learning and Artificial intelligence together.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Axioms for probability and belief-function propagation

Prakash P. Shenoy;Glenn Shafer.

uncertainty in artificial intelligence **(1990)**

686 Citations

A causal mapping approach to constructing Bayesian networks

Sucheta Nadkarni;Prakash P. Shenoy.

decision support systems **(2004)**

385 Citations

Propagating Belief Functions with Local Computations

Prakash P. Shenoy;Glenn Shafer.

IEEE Intelligent Systems **(1986)**

352 Citations

Valuation-based systems for Bayesian decision analysis

Prakash P. Shenoy.

Operations Research **(1992)**

351 Citations

A Bayesian network approach to making inferences in causal maps

Sucheta Nadkarni;Prakash P Shenoy.

European Journal of Operational Research **(2001)**

332 Citations

Propagating belief functions in qualitative Markov trees

Glenn Shafer;Prakash P. Shenoy;Khaled Mellouli.

International Journal of Approximate Reasoning **(1987)**

295 Citations

A valuation-based language for expert systems

P. P. Shenoy.

International Journal of Approximate Reasoning **(1989)**

260 Citations

On coalition formation: a game-theoretical approach

Prakash P. Shenoy.

International Journal of Game Theory **(1979)**

257 Citations

Using Bayesian networks for bankruptcy prediction: Some methodological issues

Lili Sun;Prakash P. Shenoy.

European Journal of Operational Research **(2007)**

230 Citations

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

Barry R. Cobb;Prakash P. Shenoy.

International Journal of Approximate Reasoning **(2006)**

222 Citations

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