What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (30.86%)
- Bayesian network (23.46%)
- Mathematical economics (20.99%)
What were the highlights of his more recent work (between 2010-2020)?
- Theoretical computer science (19.14%)
- Bayesian network (23.46%)
- Mathematical economics (20.99%)
In recent papers he was focusing on the following fields of study:
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.
Between 2010 and 2020, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
