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Solan Eilon: Borel Games

Borel Games

Causal Inference and Machine Learning

These lecture notes offer a general overview of a two-dimensional topological field theory called the Poisson Sigma Model. It is based on a series of talks given by the author at the University of California, Berkeley and the University of Massachusetts, Amherst in the fall of 2018 and the spring of 2019. This book contains an introduction to symplectic, Poisson and graded geometry, the path space construction for Lie algebroids via the reduced phase space of the Poisson Sigma Model, and the Cattaneo-Felder interpretation of Kontsevich's star product via Feynman diagrams. It also describes in detail particular cases of the Poisson Sigma Model, including constant, linear (2-dimensional BF-theory) and quadratic Poisson structures. The target audience of this book is graduate students and researchers with an interest in the mathematical aspects of classical and quantum field theory. No prior exposure to physics or Poisson geometry is required.

Introduction To The Poisson Sigma Model, An

An Introduction to Stochastic Modeling

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

Chicago Price Theory, 2nd Edition

Scaling (zooming in or out) is taking a close or distant view of an object. Infinite scaling (the scaling parameter tends to zero or infinity) may lead to a limit which is self-similar and much simpler than the original object. For instance, the classical Donsker's theorem says that all weakly dependent stationary processes with finite variance scale to Brownian motion at large scales. In the case of a random field indexed by two-dimensional parameter, both types of scaling can be anisotropic, meaning that the horizontal and vertical axes are scaled at different rate determined by the ratio γ&gt;0 of the scaling exponents along the axes. The natural questions under such scaling are whether the scaling limits exist for any γ&gt;0, and what are these limits are. The book tries to answer these questions. It introduces the concept of scaling transition and discusses its existence for a natural class of planar random fields including Gaussian, linear and some nonlinear ones. The scaling limits are identified and exhibit a surprising trichitomy at a critical point γ0, with 'unbalanced' limits having unusual path properties and dependence structure along one of the coordinate axes. Scaling transition occurs in applied sciences (telecommunications and econometrics) when aggregating independent processes with long-range dependence in which case 'unbalanced' limits are classical Gaussian or stable random fields.

Anisotropic Scaling Of Random Fields With Long-range Dependence: Scaling Limits Of Random Fields With Applications

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health

Methods and Applications

Differential and Algorithmic Intelligent Game Theory

Probability theory is a discipline that studies the quantitative regularity of random phenomena. The fact that random phenomena arise, especially in the era of big data and artificial intelligence, determines the importance of this discipline. This volume introduces various concepts that quantitatively describe random phenomena, including probability, random variables, distribution functions, density functions, mathematical expectations, variances, moments, and characteristic functions. It finishes off by presenting probability limit theory, including various convergences.Throughout the volume, great importance is attached to the elaboration of probability thoughts. For this reason, some practical examples to illustrate the introduced concept are always used. In order to meet the needs of different levels of readers, there is a section on Supplements and Notes at the end of each chapter to enhance and expand the content in the body of the textbook. This volume contains a large number of problems of varying levels for the reader with the purpose to review, consolidate, deepen and expand their knowledge.As the only branch of mathematics that studies the quantitative regularity of random phenomena, probability theory has not only theoretical significance, but it is also a main theoretical basis of mathematical statistics. Therefore, it will be of interest to scholars from other disciplines related to random phenomena.

Probability Theory: An Elementary Course

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

This book presents an overview of the diverse literature on change-point methodology and describes some unifying themes and recent breakthroughs. It can be used as a second-year Ph.D. level course in statistics. It is expected to be useful to researchers and graduate students, not only in statistics but also in engineering, biomedicine, economics and finance.

Change-point Methodology and Applications

Differential algebra explores properties of solutions of systems of (ordinary or partial, linear or non-linear) differential equations from an algebraic point of view. It includes as special cases algebraic systems as well as differential systems with algebraic constraints. This algebraic theory of Joseph F Ritt and Ellis R Kolchin is further enriched by its interactions with algebraic geometry, Diophantine geometry, differential geometry, model theory, control theory, automatic theorem proving, combinatorics, and difference equations. Differential algebra now plays an important role in computational methods such as symbolic integration and symmetry analysis of differential equations. These proceedings consist of tutorial and survey papers presented at the Second International Workshop on Differential Algebra and Related Topics at Rutgers University, Newark in April 2007. As a sequel to the proceedings of the First International Workshop, this volume covers more related subjects, and provides a modern and introductory treatment to many facets of differential algebra, including surveys of known results, open problems, and new, emerging, directions of research. It is therefore an excellent companion and reference text for graduate students and researchers.

Differential Algebra and Related Topics II

As the first book that systematically combines game theory and signal detection theory to provide a foundation for new research, Cooperation in Prisoners' Dilemmas provides a unique perspective on the topic of cooperation in decision making. This volume shows how contingent cooperators will evolve from any initial population through any payoff monotonic process (like replicator or logistic population dynamics), and remain stable to invasion by other players, including always defecting players. These properties are robust to mimicry and low quality signals, whenever they can be elicited from other players better than pure chance. These properties hold for pure one-shot prisoners' dilemmas: meaning no repeat interactions or information about past behavior is involved, all communication arises only from signals detected after strangers meet the first time; and no subjective preferences for fairness or reciprocity affect the raw dynamics. However, the raw dynamics will evolve such preferences if they motivate players to act contingently toward signals elicited from another player better than pure chance. Finally, the author concludes with derived predictions about the equilibrium frequency of cooperation.

Cooperation In Prisoners' Dilemmas: The Basic Case Of One-shot Prisoners' Dilemmas

Gibbs Measures In Machine Learning

Borel Games

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