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Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Complex Systems, Chaos and Self-Organisation,    Modeling and Simulation

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The fundamental and simple model of Stochastic Games was introduced in the fifties by prominent mathematicians (Shapley, Bellman) and form today a well adopted and thoroughly studied notion, with applications in computer science, mathematics, economics, and beyond. This wide adoption implies that many different research communities are involved in the study of stochastic games, oftentimes with different but related goals. Each community develops different tools towards understanding stochastic games, leading to a very broad and diverse literature with a variety of techniques and approaches. The goal of this Dagstuhl Seminar is to bring together researchers interested in the algorithmic aspects of Stochastic Games, from a theoretical as well as practical perspective.

The Bernoulli–IMS World Congress in Probability and Statistics is held every four years.

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Algebra,    Geometry and Topology

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.

Information & Knowledge Management, Big Data Computing,    Scientific Computing and Data Science

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Non-Bayesian approaches appear to solve many problems that Bayesians once dreamt of solving using Bayesian methods. We thus believe that it is timely and important to rethink and redefine the promises and challenges of Bayesian approaches; and also to elucidate which Bayesian methods might prevail against their non-Bayesian competitors; and finally identify key application areas where Bayes can shine.