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Michael (Misha) Chertkov: Principles And
Methods Of Applied Mathematics

Principles And Methods Of Applied Mathematics

Extended Hypergeometric Functions and Orthogonal Polynomials

Thermal and Flow Measurements spans the basics of measurement methods to the advanced diagnostic applications in experiments and testing. It provides a foundational understanding of how to measure flow rates, temperature, pressure and other key parameters in fluid and thermal processes.Offering in-depth technical treatments of underlying physics, ranging from optics and electromagnetics to quantum mechanics, the book demonstrates how to design and apply measurements in mechanical, aerospace, and chemical engineering processes. It features new sections on image-based particle velocimetry, high-speed velocimetry, satellite-based methods for scaled measurements, and AI in image analysis, advanced laser diagnostics, and remote sensing. It also includes end-of-chapter problems and exercises.The book is intended for senior undergraduate and graduate engineering students studying experimental methods and measurement.Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.

Thermal and Flow Measurements

For Doctoral Level Physics

A First Graduate Course in Quantum Field Theory

The aim of this textbook is to improve students' ability to formulate economic problems and provide essential mathematical tools for analysis across various contexts. This book presents the author's current understanding of mathematics, shaped by several years of teaching this subject as well as enriched through interactions with his students. To aid students in their learning, the book is written in a manner that better highlights key concepts and provides additional clarity when possible. In this way, it is hoped that students will feel more guided in their study of mathematics and its applications in microeconomics.

Essential Mathematics For Microeconomics: A Student's Guide To Key Mathematical Techniques

De Gruyter Studies in Mathematics

Orthogonal Polynomials

Volume II

Cryptocurrency Forensics and Investigation using Open Source Intelligence Techniques (OSINT)

While the field of Deep Learning has been advancing at frightening speeds during the last ten years, we do not have a plausible theoretical explanation of its success yet. One can safely say that Deep Learning works, but nobody really understands why. Nevertheless, starting around five years before now, a decent number of theoretical papers specifically concerning neural nets started to emerge at top machine learning venues. This makes us think that the core of Deep Learning theory has already started to crystallize.The goal of the present book is to present these core concepts of Deep Learning theory to readers so that they could directly dive into recent papers of this area. For this purpose, each chapter elaborates a simple model or a classical result in details first and then discusses possible generalizations and more recent developments of the same idea.We have to warn the reader that the present book is not a mathematical manuscript. Not all results here are stated as formal theorems and not all theorems are provided with complete and rigorous proofs. Many of the theorems of the book are proven up to some technical lemmas, while for some there is only a proof sketch. This conforms with the main idea of the book: present and illustrate concepts rather than reproduce all the results.The book, in its present form, covers the following topics: uniform generalization bounds, PAC-bayesian generalization bounds, double descent phenomena, infinitely-wide networks, implicit bias of gradient descent, loss landscape, gradient descent convergence guarantees, and initialization strategies.

Elements Of Deep Learning Theory

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

This book presents a comprehensive treatment of pricing and hedging soccer betting contracts using the modern techniques of probability, statistics, finance, and portfolio management. It introduces betting in general and then addresses the probabilistic intuition needed in the more complex world of soccer betting. The author presents an innovative perspective by thinking about betting odds in terms of the expected number of goals of both teams. In addition, when the betting market is in play, the book treats betting before the game and during the game separately.

This book presents a comprehensive treatment of pricing and hedging soccer betting contracts using the modern techniques of probability, statistics, finance, and portfolio management. It introduces betting in general and then addresses the probabilistic intuition needed in the more complex world of soccer betting. The author presents an innovative perspective by thinking about betting odds in terms of the expected number of goals of both teams. In addition, when the betting market is in play, the book treats betting before the game and during the game separately.

Science of Soccer Statistics

Soccermetrics

Calculus for the Curious

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

Storytelling with Data Box Set

Principles And Methods Of Applied Mathematics

Bibliografische Angaben

Schlagworte

Bibliografische Angaben

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