Ninth school of the Society for Imprecise Probability: Theories and Applications (SIPTA)

The Risk Institute is hosting the next SIPTA school. The school will be online. Register your interest here or below.

We warmly invite all the students and researchers interested in imprecise probability (IP) to participate. If you are new to IP and wonder what it is and whether you’re interested, here is an introduction.

It is an exciting time to be part of the IP community because of the many challenges we are faced with in modelling complex autonomous systems, and in making sense of experimental data.

The theory of imprecise probability

In a classical textbook a famous physics professor said: “We need to be very certain about what we mean by uncertainty if we want to quantify it”. In physics and engineering uncertainty is often modelled by probability, and calculated using methods from probability theory. But can we be certain about probabilities? What if there was a theory that encompassed probability, but went beyond that. A theory that generalizes probability making it more flexible to accommodate the complexity of the world. Surely if such a theory existed, we would be more certain about what we mean by uncertainty. We would not need to restrict our views to a mathematical model just because say we are comfortable with it. Luckily this theory exists, is called imprecise probability, and is going to revolutionise the way we understand and quantify uncertainty.

December 2020:

Tuesday 8th December:
12-4pm CET (11am-3pm GMT)

Fabio Cozman & Denis Deretani Maua

Wednesday 9th December:
3-7pm CET (2-6pm GMT)

Glenn Shafer

Thursday 10th December:
3-7pm CET (2-6pm GMT)

Glenn Shafer

January 2021:

Tuesday 26th January & Wednesday 27th January
1-5pm GMT

Ignacio Montes

Wednesday 27th January 3-5pm GMT & Thursday 28th January 1-5pm GMT

Ryan Martin

February 2021:

Wednesday 10th February

Seamus Bradley

Date TBC

Kari Sentz


Dr. Fabio Gagliardi Cozman

Fabio Cozman: Credal Networks: Specification, Algorithms, Complexity

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Fabio G. Cozman is a Full Professor at Escola Politécnica, Universidade de São Paulo (USP), Director of the Center for Artificial Intelligence at USP, and researcher with an interest in machine learning and knowledge/uncertainty representation. Engineer (USP) and PhD (Carnegie Mellon University, USA), he has served, amongst other duties, as Program and General Chair of the Conference on Uncertainty in Artificial Intelligence, Area Chair of the International Joint Conference on Artificial Intelligence, and Associate Editor of the Artificial Intelligence Journal, the Journal of Artificial Intelligence Research, and the Journal of Approximate Reasoning. He chaired the Special Committee on Artificial Intelligence of the Brazilian Computer Society, and received the Prize for Scientific Merit in Artificial Intelligence by that society.

Dr. Denis D. Maua

Denis D. Mauá is an Assistant Professor at the Department of Computer Science of the Institute of Mathematics and Statistics of the University of São Paulo. He obtained his PhD from the University of Lugano (2013), and his Masters (2009) and Engineering degrees (2007) from the University of São Paulo. He has authored and co-authored over 50 articles in journals and conference proceedings.
His research focus on the theory of probabilistic reasoning, and its application to artificial intelligence and machine learning.

Dr. Glenn Shafer

Glenn Shafer: Game theoretic foundations for statistical testing and imprecise probablities

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Dr. Glenn Shafer is the co-creator of the Dempster-Shafer Theory. He is a University Professor and Board of Governors Professor at Rutgers University. He received a bachelor's degree in mathematics from Princeton University, then entered the Peace Corps, serving in Afghanistan. He returned to Princeton, earning a PhD in mathematical statistics in 1973. During the 1970s and 1980s he expanded a theory first introduced by Arthur P. Dempster to create the Dempster-Shafer Theory, also described as the theory of belief functions or evidence theory. It is a general framework for reasoning with uncertainty. The theory and its extensions have been of particular interest to the artificial intelligence community

Dr. Ryan Martin

Ignacio Montes: Introduction to imprecise probability

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Ignacio Montes is an Assistant Professor at the Department of Statistics and Operations Research at the University of Oviedo. He got the PhD degree on Mathematics and Statistics in 2014 with a thesis entitled “Comparison of alternatives under uncertainty and imprecision”. After finishing his PhD, he was a postdoctoral student at the HEUSIASYC Research Unit of the Technologic University of Compiegne and an Assistant Professor at the Carlos III University of Madrid. His research focuses on different theoretical aspects of the imprecise probability theory.

Dr. Ryan Martin

Ryan Martin: Inferential models

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Dr. Ryan Martin is a Professor in the Department of Statistics at North Carolina State University, USA. His research interests include asymptotics, empirical Bayes analysis, high- and infinite dimensional inference problems, foundations of statistics, imprecise probability, mixture models, etc. He is co-author of the monograph Inferential Models and co-founder of the Researchers. One peer review and publication platform.

Dr. Ryan Martin

Seamus Bradley: Expert aggregation

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Dr. Seamus Bradley is currently a Marie Curie Individual Fellow at the University of Leeds, with interests in Epistemology, Decision Theory, Philosophy of Science and Logic. Before working at the University of Leeds, Dr Bradley was an assistant professor at the University of Tilburg Philosophy Department, and before that was a postdoctoral research fellow at the Munich Centre for Mathematical Philosophy. Dr Bradley completed a philosophy PhD at the London School of Economics in 2012 with a thesis title of "Scientific Uncertainty and Decision Making”.