Meetings/Workshops on Probability and Statistics, Game Theory in Canada

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The systematic study of random graphs commenced with the seminal work of Erd" os and Renyi in the 1960s. Beyond the classical connections and synergies between random graphs and computer science, over the last few years a new exciting connection between random graphs and statistical inference has been discovered. These connections have led to important new insights on computational problems and statistical inference problems and to new methods for the analysis of random graphs or random discrete structures in general. In addition, since the late 1990s an insightful approach called the cavity method by physicists has been applied to put forward predictions on structural properties of random graphs and their phase transitions. More recently, it has inspired new algorithms for statistical inference problems such as message passing algorithms and novel spectral algorithms. With this workshop we hope to stimulate research and foster new exciting interdisciplinary collaborations between researchers with different backgrounds and expertise in random graphs, computer science, probability, statistics, statistical inference and information theory.

Courses and Events for Math Students and Early Career Researchers,    Graph Theory and Combinatorics

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Advances in data collection and social media have led to network and graph data becoming available in many areas, including social sciences, biological sciences, and engineering. Understanding and modeling network structure, as well as conducting rigorous statistical inference to assess uncertainty, can provide crucial insights into the dynamics and interaction mechanisms of the system. Statistical network analysis to date has largely focused on the setting where a single network is observed as a noisy version of some underlying structure of interest. This setting in itself is challenging, requiring adaptation of existing statistical frameworks to networks and bridging the gap between theoretically optimal performance and computational feasibility. The workshop will start by covering recent advances in this setting.

Courses and Events for Math Students and Early Career Researchers,    Graph Theory and Combinatorics

The Fields Institute for Research in Mathematical Sciences

This conference will be held to celebrate the 80th birthday of French probabilist Gérard Letac on January 6, 2020. Continuously active and publishing since his first paper appeared in 1964, Letac is now Professor Emeritus at the Institut de Mathématiques de Toulouse, Laboratoire de Statistique et Probabilités of the Université Paul Sabatier. He has authored five books and directed twenty-six PhD students.

Letac’s work in the areas of probability theory, mathematical statistics, measure theory and analysis is well recognized by both the mathematical and statistical communities. His papers have had substantial impact on many probabilists and statisticians. In his talks, Letac often reveals surprising connections and elegant structures underlying problems that are either complex or deceptively innocent looking. Due to his breadth of expertise, he also often uncovers a deep algebraic structure behind probability problems. Former students and colleagues will celebrate Gérard Letac's work during this conference and we hope that the breadth as well as depth of his work will inspire the upcoming generation of young researchers.

The conference includes daily tutorial lectures accessible to graduate students.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Nonlinear inverse problems are ubiquitous in medical sciences, engineering, physics and other natural sciences. This workshop will advance a statistical paradigm that allows underpinning statistical decision making in such problems in a scientifically rigorous way. This will be achieved by providing `frequentist' large sample guarantees for inferences arising from commonly used (Bayesian or non-Bayesian) algorithms in such problems. Thereby, a large cluster of applied problems, where algorithms are used for day-to-day decision making with statistical data, will be put on a solid and robust foundation, facilitating its confident use in modern society.

Courses and Events for Math Students and Early Career Researchers,    Algebra

American Statistical Association