What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
David Poole mainly investigates Artificial intelligence, Bayesian network, Mathematical economics, Independence and Machine learning.
His study in the field of Non-monotonic logic, Probabilistic logic and Logic programming is also linked to topics like Default logic.
The various areas that he examines in his Probabilistic logic study include Evidential reasoning approach, Expert system and Horn clause.
His Bayesian network study combines topics in areas such as Representation, Conditional probability, Markov decision process, Mathematical optimization and Posterior probability.
His Mathematical economics research is multidisciplinary, incorporating perspectives in Ceteris paribus and Preference.
His research in Machine learning intersects with topics in Theoretical computer science, Joint probability distribution, Independence, Variable-order Bayesian network and Key.
His most cited work include:
- CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements (822 citations)
- A logical framework for default reasoning (667 citations)
- Probabilistic Horn abduction and Bayesian networks (540 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Theoretical computer science, Probabilistic logic, Machine learning and Bayesian network are his primary areas of study.
Artificial intelligence and Default logic are two areas of study in which David Poole engages in interdisciplinary work.
His Theoretical computer science research incorporates themes from Random variable, Embedding, Representation, Inference and Graph.
In his work, Ceteris paribus and Preference is strongly intertwined with Independence, which is a subfield of Inference.
His work deals with themes such as Ontology, Graphical model and Algorithm, which intersect with Probabilistic logic.
His Bayesian network research incorporates elements of Bayesian statistics, Conditional probability, Posterior probability, Bayesian probability and Chain rule.
He most often published in these fields:
- Artificial intelligence (45.64%)
- Theoretical computer science (24.07%)
- Probabilistic logic (21.16%)
What were the highlights of his more recent work (between 2012-2021)?
- Theoretical computer science (24.07%)
- Artificial intelligence (45.64%)
- Probabilistic logic (21.16%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Theoretical computer science, Artificial intelligence, Probabilistic logic, Machine learning and Representation.
His research in Theoretical computer science intersects with topics in Hypergraph, Embedding, Inference, Relation and Graph.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Random variable, that overlap with his field of interest in Range.
His Probabilistic logic research is multidisciplinary, incorporating perspectives in Statistical model and Data set.
David Poole combines subjects such as Conditional probability and Statistical relational learning with his study of Machine learning.
His biological study spans a wide range of topics, including Set, Simple, Influence diagram and Negation.
Between 2012 and 2021, his most popular works were:
- SimplE embedding for link prediction in knowledge graphs (142 citations)
- SimplE Embedding for Link Prediction in Knowledge Graphs (55 citations)
- Relational logistic regression (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
David Poole mainly investigates Theoretical computer science, Probabilistic logic, Logistic regression, Artificial intelligence and Embedding.
The various areas that David Poole examines in his Theoretical computer science study include Conditional probability, Representation, Data structure and Graph.
The study incorporates disciplines such as Bayesian network, Inference and Random variable in addition to Probabilistic logic.
His study in the fields of Probabilistic inference under the domain of Inference overlaps with other disciplines such as Low-level programming language.
His Logistic regression study deals with the bigger picture of Machine learning.
The Graphical model research David Poole does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Naive Bayes classifier, therefore creating a link between diverse domains of science.
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