A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
Vladimir Naumovich Vapnik is a computer scientist, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms.
Judea Pearl is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks. He is also credited for developing a theory of causal and counterfactual inference based on structural models. In 2011, the Association for Computing Machinery (ACM) awarded Pearl with the Turing Award, the highest distinction in computer science, "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning". He is the author of several books, including the technical Causality: Models, Reasoning and Inference, and The Book of Why, a book on causality aimed at the general public.
A dynamic Bayesian network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps.
Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
Alexander Philip Dawid is Emeritus Professor of Statistics of the University of Cambridge, and a Fellow of Darwin College, Cambridge. He is a leading proponent of Bayesian statistics.
Formal epistemology uses formal methods from decision theory, logic, probability theory and computability theory to model and reason about issues of epistemological interest. Work in this area spans several academic fields, including philosophy, computer science, economics, and statistics. The focus of formal epistemology has tended to differ somewhat from that of traditional epistemology, with topics like uncertainty, induction, and belief revision garnering more attention than the analysis of knowledge, skepticism, and issues with justification.
Zoubin Ghahramani FRS is a British-Iranian researcher and Professor of Information Engineering at the University of Cambridge. He holds joint appointments at University College London and the Alan Turing Institute. and has been a Fellow of St John's College, Cambridge since 2009. He was Associate Research Professor at Carnegie Mellon University School of Computer Science from 2003 to 2012. He was also the Chief Scientist of Uber from 2016 until 2020. He joined Google Brain in 2020 as senior research director. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence.
Andrew Blake is a British scientist, former laboratory director of Microsoft Research Cambridge and Microsoft Distinguished Scientist, former director of the Alan Turing Institute, Chair of the Samsung AI Centre in Cambridge, honorary professor at the University of Cambridge, Fellow of Clare Hall, Cambridge, and a leading researcher in computer vision.
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use first-order logic to describe relational properties of a domain in a general manner and draw upon probabilistic graphical models to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
Peter William O'Hearn, formerly a research scientist at Meta, is a Distinguished Engineer at Lacework and a Professor of Computer science at University College London (UCL). He has made significant contributions to formal methods for program correctness. In recent years these advances have been employed in developing industrial software tools that conduct automated analysis of large industrial codebases.
Marta Zofia Kwiatkowska is a Polish theoretical computer scientist based in the United Kingdom.
Nir Friedman is an Israeli Professor of Computer Science and Biology at the Hebrew University of Jerusalem.
Stuart Alan Geman is an American mathematician, known for influential contributions to computer vision, statistics, probability theory, machine learning, and the neurosciences. He and his brother, Donald Geman, are well known for proposing the Gibbs sampler, and for the first proof of convergence of the simulated annealing algorithm.
Richard Eugene Neapolitan was an American scientist. Neapolitan is most well-known for his role in establishing the use of probability theory in artificial intelligence and in the development of the field Bayesian networks.
Alan Yuille is a Bloomberg Distinguished Professor of Computational Cognitive Science with appointments in the departments of Cognitive Science and Computer Science at Johns Hopkins University. Yuille develops models of vision and cognition for computers, intended for creating artificial vision systems. He studied under Stephen Hawking at Cambridge University on a PhD in theoretical physics, which he completed in 1981.
Michael J. Black is an American-born computer scientist working in Tübingen, Germany. He is a founding director at the Max Planck Institute for Intelligent Systems where he leads the Perceiving Systems Department in research focused on computer vision, machine learning, and computer graphics. He is also an Honorary Professor at the University of Tübingen.
Thomas L. Griffiths is an Australian academic who is the Henry R. Luce Professor of Information Technology, Consciousness, and Culture at Princeton University. He studies human decision-making and its connection to problem-solving methods in computation. His book with Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions, was named one of the "Best Books of 2016" by MIT Technology Review.
Vladimir Vovk is a British computer scientist, and professor at Royal Holloway University of London. He is the co-inventor of Conformal prediction and is known for his contributions to the concept of E-values. He is the co-director of the Centre for Machine Learning at Royal Holloway University of London, and a Fellow of the Royal Statistical Society.
