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The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields.

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you'll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.

Data analysis has been a hot topic for a number of years, and many future data scientists have backgrounds that are relatively light in mathematics. This slim volume provides a very approachable guide to the techniques of the subject, designed with such people in mind. Formulae are kept to a minimum, but the book's scope is broad, introducing the basic ideas of probability and statistics and more advanced techniques such as generalised linear models, classification using logistic regression, and support-vector machines. An essential feature of the book is that it does not tie to any particular software. The methods introduced in this book could also be implemented using any other statistical software and applying any major statistical package. Academically, the book amounts to a first course, practical for those at the undergraduate level, either as part of a mathematics/statistics degree or as a data-oriented option for a non-mathematics degree. The book appeals to would-be data scientists who may be formula shy. However, it could also be a relevant purchase for statisticians and mathematicians, for whom data science is a new departure, overall appealing to any computer-literate reader with data to analyse.

Dieses Praktikerbuch ist zum einen eine anwendungsnahe Einführung in die Wahrscheinlichkeitstheorie und Statistik. Zum anderen erklärt es die statistische Versuchsplanung, die für die Planung und saubere Auswertung von Versuchsreihen von entscheidender Wichtigkeit ist. Herleitungen und Beweise werden dabei ausführlich erläutert, ohne sich in mathematischen Details zu verlieren. In mehr als 160 Beispielen illustriert das Buch die Umsetzung alltagssprachlich formulierter Probleme in wahrscheinlichkeitstheoretische bzw. statistische Modelle - und deren Implementierung in R und SAS.

This volume is an original collection of articles by 44 leading mathematicians on the theme of the future of the discipline. The contributions range from musings on the future of specific fields, to analyses of the history of the discipline, to discussions of open problems and conjectures, including first solutions of unresolved problems. Interestingly, the topics do not cover all of mathematics, but only those deemed most worthy to reflect on for future generations. These topics encompass the most active parts of pure and applied mathematics, including algebraic geometry, probability, logic, optimization, finance, topology, partial differential equations, category theory, number theory, differential geometry, dynamical systems, artificial intelligence, theory of groups, mathematical physics and statistics.

The Price is Right is television's longest-running game show. Since its inception in 1956, contestants have won cars, tropical vacations, diamond jewelry, even a live horse, and the hosts'excited catchphrase “come on down!” has become part of our everyday vernacular. Part of the program's enduring appeal is the apparent ease of the game, guessing the cash value of certain prizes. But, if that's the case, then why do so many contestants come away from the show empty-handed?Solving The Price is Right is an in-depth exploration of the underlying probability theory of the popular television program that explores how biases and behavioral pitfalls limit our ability to successfully apply logic and math both on and off the show. With rigorous data and analysis compiled from Seasons 47 and 48 (356 total episodes), investor and math practitioner Justin L. Bergner draws strategic and mathematical insights from all facets of the show, from Contestant's Row bidding to the Showcase Showdown, and all 77 Pricing Games, using a combination of game theory, probability theory, statistics, and pattern recognition. In each section, Bergner summarizes contestant performance, highlights the biases leading to sub-par outcomes, and shows how outcomes can be improved by executing the right strategies while avoiding cognitive biases. Throughout, Bergner applies the lessons learned to the fields of business, finance, and our real lives, shedding light on themes of reverse psychology, strategic patience, and the importance of establishing what is sufficient for success in our pursuits. The result is a truly unique and meticulously researched book that uses Solving The Price is Right as a lens to examine our own choices – and how to make better ones.

確率・統計の理論と応用を，予備知識を前提とせず基礎からわかりやすく述べる．確率法則，統計分布，推定と検定などを豊富な例題をまじえて明快に解説し，さらに新しい統計学の成果である情報量規準AICと時系列解析などの適用例をていねいに説明する．演習問題・解答により徹底理解と応用力が得られる．

Historians constantly wrestle with uncertainty, never more so than when attempting quantification, yet the field has given little attention to the nature of uncertainty and strategies for managing it. This volume proposes a powerful new approach to uncertainty in ancient history, drawing on techniques widely used in the social and natural sciences. It shows how probability-based techniques used to manage uncertainty about the future or the present can be applied to uncertainty about the past. A substantial introduction explains the use of probability to represent uncertainty. The chapters that follow showcase how the technique can offer leverage on a wide range of problems in ancient history, from the incidence of expropriation in the Classical Greek world to the money supply of the Roman empire.

Does timing, circumstance, or luck impact your health care? This groundbreaking book reveals the hidden side of medicine and how unexpected—but predictable—events can profoundly affect our health. • Is there ever a good time to have a heart attack? Why do kids born in the summer get diagnosed more often with A.D.H.D.? How are marathons harmful for your health, even when you're not running?'Fantastically entertaining and deeply thought-provoking.'—Emily Oster, New York Times bestselling author of The Family Firm, Cribsheet, and Expecting Better'Random Acts of Medicine shows that the ingenious use of natural experiments can improve medicine and save lives.'—Wall Street JournalAs a University of Chicago–trained economist and Harvard medical school professor and doctor, Anupam Jena is uniquely equipped to answer these questions. And as a critical care doctor at Massachusetts General who researches health care policy, Christopher Worsham confronts their impact on the hospital's sickest patients. In this singular work of science and medicine, Jena and Worsham show us how medicine really works, and its effect on all of us.Relying on ingeniously devised natural experiments—random events that unknowingly turn us into experimental subjects—Jena and Worsham do more than offer readers colorful stories. They help us see the way our health is shaped by forces invisible to the untrained eye. Is there ever a good time to have a heart attack? Do you choose the veteran doctor or the rookie? Do you really need the surgery your doctor recommends? These questions are rife with significance; their impact can be life changing. Addressing them in a style that's both animated and enlightening, Random Acts of Medicine empowers you to see past the white coat and find out what really makes medicine work—and how it could work better.

This book provides a systematic treatment of Locke's theory of probable assent, and shows how the theory applies to Locke's philosophy of science, moral epistemology, and religious epistemology.There is a powerful case to be made that the most important dimension of Locke's philosophy is his theory of rational probable assent, rather than his theory of knowledge. According to Locke, we largely live our lives in the “twilight of probability” rather than in “the sunshine of certain knowledge.” Locke's theory of probable assent has far-reaching significance insofar as it contains a wealth of novel, independently interesting, and prescient elements that precede the modern field of formal epistemology. In this book, the author argues for the central role of probable assent in Locke's philosophy. Locke's theory of probable assent is based on an epistemic modesty that claims, roughly, that our cognitive abilities are limited and that we ought to carry ourselves in believing with due caution. This modesty motivates the author's discussion of other aspects of Locke's epistemology, notably his principle of proportionality, his doxastic involuntarism, his epistemological pragmatism, and his theory of testimony. The book concludes by connecting the theory of probable assent with Locke's views on the limits of science, moral epistemology, and the rationality of faith.Locke's Twilight of Probability will appeal to scholars and advanced students working on Locke and the history of early modern philosophy.

A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

The stochastic Maxwell equations play an essential role in many fields, including fluctuational electrodynamics, statistical radiophysics, integrated circuits, and stochastic inverse problems. This book provides some recent advances in the investigation of numerical approximations of the stochastic Maxwell equations via structure-preserving algorithms. It presents an accessible overview of the construction and analysis of structure-preserving algorithms with an emphasis on the preservation of geometric structures, physical properties, and asymptotic behaviors of the stochastic Maxwell equations. A friendly introduction to the simulation of the stochastic Maxwell equations with some structure-preserving algorithms is provided using MATLAB for the reader's convenience. The objects considered in this book are related to several fascinating mathematical fields: numerical analysis, stochastic analysis, (multi-)symplectic geometry, large deviations principle, ergodic theory, partial differential equation, probability theory, etc. This book will appeal to researchers who are interested in these topics.

This book offers a comprehensive examination of the Cramér–Lundberg model, which is the most extensively researched model in ruin theory. It covers the fundamental dynamics of an insurance company's surplus level in great detail, presenting a thorough analysis of the ruin probability and related measures for both the standard model and its variants.Providing a systematic and self-contained approach to evaluate the crucial quantities found in the Cramér–Lundberg model, the book makes use of connections with related queueing models when appropriate, and its emphasis on clean transform-based techniques sets it apart from other works. In addition to consolidating a wealth of existing results, the book also derives several new outcomes using the same methodology.This material is complemented by a thoughtfully chosen collection of exercises. The book's primary target audience is master's and starting PhD students in applied mathematics, operations research, and actuarial science, although it also serves as a useful methodological resource for more advanced researchers. The material is self-contained, requiring only a basic grounding in probability theory and some knowledge of transform techniques.

Over the course of his distinguished career, Robert Strichartz (1943-2021) had a substantial impact on the field of analysis with his deep, original results in classical harmonic, functional, and spectral analysis, and in the newly developed analysis on fractals. This is the first volume of a tribute to his work and legacy, featuring chapters that reflect his mathematical interests, written by his colleagues and friends. An introductory chapter summarizes his broad and varied mathematical work and highlights his profound contributions as a mathematical mentor. The remaining articles are grouped into three sections – functional and harmonic analysis on Euclidean spaces, analysis on manifolds, and analysis on fractals – and explore Strichartz'contributions to these areas, as well as some of the latest developments.

This book develops limit theorems for a natural class of long range random walks on finitely generated torsion free nilpotent groups. The limits in these limit theorems are Lévy processes on some simply connected nilpotent Lie groups. Both the limit Lévy process and the limit Lie group carrying this process are determined by and depend on the law of the original random walk. The book offers the first systematic study of such limit theorems involving stable-like random walks and stable limit Lévy processes in the context of (non-commutative) nilpotent groups.

This book deals with two main topics. The first is a theory that aims to unify the many interpretations of probability presented in the literature. The second uses this comprehensive theory of probability to answer the questions of quantum mechanics that have long been debated. The entire book proposes original solutions that several experimental cases substantiate.

This book provides a compact and systematic overview of closure properties of heavy-tailed and related distributions, including closure under tail equivalence, convolution, finite mixing, maximum, minimum, convolution power and convolution roots, and product-convolution closure. It includes examples and counterexamples that give an insight into the theory and provides numerous references to technical details and proofs for a deeper study of the subject. The book will serve as a useful reference for graduate students, young researchers, and applied scientists.

This book presents the latest developments in the theory of gradient flows in random walk spaces. A broad framework is established for a wide variety of partial differential equations on nonlocal models and weighted graphs. Within this framework, specific gradient flows that are studied include the heat flow, the total variational flow, and evolution problems of Leray-Lions type with different types of boundary conditions. With many timely applications, this book will serve as an invaluable addition to the literature in this active area of research.Variational and Diffusion Problems in Random Walk Spaces will be of interest to researchers at the interface between analysis, geometry, and probability, as well as to graduate students interested in exploring these areas.

Sergio Albeverio gave important contributions to many fields ranging from Physics to Mathematics, while creating new research areas from their interplay. Some of them are presented in this Volume that grew out of the Random Transformations and Invariance in Stochastic Dynamics Workshop held in Verona in 2019. To understand the theory of thermo- and fluid-dynamics, statistical mechanics, quantum mechanics and quantum field theory, Albeverio and his collaborators developed stochastic theories having strong interplays with operator theory and functional analysis. His contribution to the theory of (non Gaussian)-SPDEs, the related theory of (pseudo-)differential operators, and ergodic theory had several impacts to solve problems related, among other topics, to thermo- and fluid dynamics. His scientific works in the theory of interacting particles and its extension to configuration spaces lead, e.g., to the solution of open problems in statistical mechanics and quantum fieldtheory. Together with Raphael Hoegh Krohn he introduced the theory of infinite dimensional Dirichlet forms, which nowadays is used in many different contexts, and new methods in the theory of Feynman path integration. He did not fear to further develop different methods in Mathematics, like, e.g., the theory of non-standard analysis and p-adic numbers.

This is the first book of two volumes covering the basics of statistical methods and analysis. Significant topics include concepts of research and data analysis, descriptive statistics, probability and distributions, correlation and regression, and statistical inference. The book includes useful examples and exercises as well as relevant case studies for proper implementation of the discussed tools. This book will be a valuable text for undergraduate students of statistics, management, economics, and psychology, wanting to gain basic understanding of statistics and the usage of its various concepts.

This textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industrial engineering and operations management students. The book offers theory in an accessible manner and includes numerous practical examples based on engineering applications.Probability Foundations for Engineers, Second Edition continues to focus specifically on probability rather than probability and statistics. It offers a conversational presentation rather than a theorem or proof and includes examples based on engineering applications as it highlights Excel computations. This new edition presents a review of set theory and updates all descriptions, such as events versus outcomes, so that they are more understandable. Additional new material includes distributions such as beta and lognormal, a section on counting principles for defining probabilities, a section on mixture distributions and a pair of distribution summary tables.Intended for undergraduate engineering students, this new edition textbook offers a foundational knowledge of probability. It is also useful to engineers already in the field who want to learn more about probability concepts. An updated solutions manual is available for qualified textbook adoptions.

Statistical Power Analysis explains the key concepts in statistical power analysis and illustrates their application in both tests of traditional null hypotheses (that treatments or interventions have no effect in the population) and in tests of the minimum-effect hypotheses (that the population effects of treatments or interventions are so small that they can be safely treated as unimportant). It provides readers with the tools to understand and perform power analyses for virtually all the statistical methods used in the social and behavioral sciences.Brett Myors and Kevin Murphy apply the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. This book starts with a review of the key concepts that underly statistical power. It goes on to show how to perform and interpret power analyses, and the ways to use them to diagnose and plan research. We discuss the uses of power analysis in correlation and regression, in the analysis of experimental data, and in multilevel studies. This edition includes new material and new power software. The programs used for power analysis in this book have been re-written in R, a language that is widely used and freely available. The authors include R codes for all programs, and we have also provided a web-based app that allows users who are not comfortable with R to perform a wide range of analyses using any computer or device that provides access to the web.Statistical Power Analysis helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out the way they do. The fifth edition includes updates to all chapters to accommodate the most current scholarship, as well as recalculations of all examples. This book is intended for graduate students and faculty in the behavioral and social sciences; researchers in other fields will find the concepts and methods laid out here valuable and applicable to studies in many domains.

This book focuses on research in linear algebra, statistics, matrices, graphs and their applications. Many chapters in the book feature new findings due to applications of matrix and graph methods. The book also discusses rediscoveries of the subject by using new methods. Dedicated to Prof. Calyampudi Radhakrishna Rao (C.R. Rao) who has completed 100 years of legendary life and continues to inspire us all and Prof. Arbind K. Lal who has sadly departed us too early, it has contributions from collaborators, students, colleagues and admirers of Professors Rao and Lal. With many chapters on generalized inverses, matrix analysis, matrices and graphs, applied probability and statistics, and the history of ancient mathematics, this book offers a diverse array of mathematical results, techniques and applications. The book promises to be especially rewarding for readers with an interest in the focus areas of applied linear algebra, probability and statistics.

This book extends the local central limit theorem to Markov chains whose state spaces and transition probabilities are allowed to change in time. Such chains are used to model Markovian systems depending on external time-dependent parameters. The book develops a new general theory of local limit theorems for additive functionals of Markov chains, in the regimes of local, moderate, and large deviations, and provides nearly optimal conditions for the classical expansions, as well as asymptotic corrections when these conditions fail. Applications include local limit theorems for independent but not identically distributed random variables, Markov chains in random environments, and time-dependent perturbations of homogeneous Markov chains.The inclusion of appendices with background material, numerous examples, and an account of the historical background of the subject make this self-contained book accessible to graduate students. It will also be useful for researchers in probability and ergodic theory who are interested in asymptotic behaviors, Markov chains in random environments, random dynamical systems and non-stationary systems.

This book offers an introduction to the field of stochastic analysis of Hermite processes. These selfsimilar stochastic processes with stationary increments live in a Wiener chaos and include the fractional Brownian motion, the only Gaussian process in this class. Using the Wiener chaos theory and multiple stochastic integrals, the book covers the main properties of Hermite processes and their multiparameter counterparts, the Hermite sheets. It delves into the probability distribution of these stochastic processes and their sample paths, while also presenting the basics of stochastic integration theory with respect to Hermite processes and sheets. The book goes beyond theory and provides a thorough analysis of physical models driven by Hermite noise, including the Hermite Ornstein-Uhlenbeck process and the solution to the stochastic heat equation driven by such a random perturbation. Moreover, it explores up-to-date topics central to current research in statistical inference for Hermite-driven models.

This book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects. Some applicationsin bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow.

This book describes extensions of Sudakov's classical result on the concentration of measure phenomenon for weighted sums of dependent random variables. The central topics of the book are weighted sums of random variables and the concentration of their distributions around Gaussian laws. The analysis takes place within the broader context of concentration of measure for functions on high-dimensional spheres. Starting from the usual concentration of Lipschitz functions around their limiting mean, the authors proceed to derive concentration around limiting affine or polynomial functions, aiming towards a theory of higher order concentration based on functional inequalities of log-Sobolev and Poincaré type. These results make it possible to derive concentration of higher order for weighted sums of classes of dependent variables.While the first part of the book discusses the basic notions and results from probability and analysis which are needed for the remainder of the book, the latter parts provide a thorough exposition of concentration, analysis on the sphere, higher order normal approximation and classes of weighted sums of dependent random variables with and without symmetries.

The main subject of the book is stochastic analysis and its various applications to mathematical finance and statistics of random processes. The main purpose of the book is to present, in a short and sufficiently self-contained form, the methods and results of the contemporary theory of stochastic analysis and to show how these methods and results work in mathematical finance and statistics of random processes. The book can be considered as a textbook for both senior undergraduate and graduate courses on this subject. The book can be helpful for undergraduate and graduate students, instructors and specialists on stochastic analysis and its applications.

This book provides an accessible overview concerning the stochastic numerical methods inheriting long-time dynamical behaviours of finite and infinite-dimensional stochastic Hamiltonian systems. The long-time dynamical behaviours under study involve symplectic structure, invariants, ergodicity and invariant measure. The emphasis is placed on the systematic construction and the probabilistic superiority of stochastic symplectic methods, which preserve the geometric structure of the stochastic flow of stochastic Hamiltonian systems. The problems considered in this book are related to several fascinating research hotspots: numerical analysis, stochastic analysis, ergodic theory, stochastic ordinary and partial differential equations, and rough path theory. This book will appeal to researchers who are interested in these topics.

This Festschrift celebrates Teddy Seidenfeld and his seminal contributions to philosophy, statistics, probability, game theory and related areas. The 13 contributions in this volume, written by leading researchers in these fields, are supplemented by an interview with Teddy Seidenfeld that offers an abbreviated intellectual autobiography, touching on topics of timeless interest concerning truth and uncertainty. Indeed, as the eminent philosopher Isaac Levi writes in this volume:'In a world dominated by Alternative Facts and Fake News, it is hard to believe that many of us have spent our life's work, as has Teddy Seidenfeld, in discussing truth and uncertainty.'The reader is invited to share this celebration of Teddy Seidenfeld's work uncovering truths about uncertainty and the penetrating insights they offer to our common pursuit of truth in the face of uncertainty.

Probability and statistics courses are more popular than ever. Regardless of your major or your profession, you will most likely use concepts from probability and statistics often in your career. The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course. The book focuses on engineering examples and applications, while also including social sciences and more examples. Depending on the chapter flows, a course can be tailored for students at all levels and background. Over many years of teaching this course, the authors created problems based on real data, student projects, and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both. R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work, in class, or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment

This textbook offers a complete one-semester course in probability, covering the essential topics necessary for further study in the areas of probability and statistics. The book begins with a review of the fundamentals of measure theory and integration. Probability measures, random variables, and their laws are introduced next, along with the main analytic tools for their investigation, accompanied by some applications to statistics. Questions of convergence lead to classical results such as the law of large numbers and the central limit theorem with their applications also to statistical analysis and more. Conditioning is the next main topic, followed by a thorough introduction to discrete time martingales. Some attention is given to computer simulation. Through the text, over 150 exercises with full solutions not only reinforce the concepts presented, but also provide students with opportunities to develop their problem-solving skills, and make this textbook suitable forguided self-study. Based on years of teaching experience, the author's expertise will be evident in the clear presentation of material and the carefully chosen exercises. Assuming familiarity with measure and integration theory as well as elementary notions of probability, the book is specifically designed for teaching in parallel with a first course in measure theory. An invaluable resource for both instructors and students alike, it offers ideal preparation for further courses in statistics or probability, such as stochastic calculus, as covered in the author's book on the topic.

Vector Quantization, a pioneering discretization method based on nearest neighbor search, emerged in the 1950s primarily in signal processing, electrical engineering, and information theory. Later in the 1960s, it evolved into an automatic classification technique for generating prototypes of extensive datasets. In modern terms, it can be recognized as a seminal contribution to unsupervised learning through the k-means clustering algorithm in data science. In contrast, Functional Quantization, a more recent area of study dating back to the early 2000s, focuses on the quantization of continuous-time stochastic processes viewed as random vectors in Banach function spaces. This book distinguishes itself by delving into the quantization of random vectors with values in a Banach space—a unique feature of its content. Its main objectives are twofold: first, to offer a comprehensive and cohesive overview of the latest developments as well as several new results in optimal quantization theory, spanning both finite and infinite dimensions, building upon the advancements detailed in Graf and Luschgy's Lecture Notes volume. Secondly, it serves to demonstrate how optimal quantization can be employed as a space discretization method within probability theory and numerical probability, particularly in fields like quantitative finance. The main applications to numerical probability are the controlled approximation of regular and conditional expectations by quantization-based cubature formulas, with applications to time-space discretization of Markov processes, typically Brownian diffusions, by quantization trees. While primarily catering to mathematicians specializing in probability theory and numerical probability, this monograph also holds relevance for data scientists, electrical engineers involved in data transmission, and professionals in economics and logistics who are intrigued by optimal allocation problems.

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This textbook covers the fundamentals of reliability theory and its application for engineering processes, especially for aircraft units and systems. Reliability basis was explained for the best understanding of reliability analysis application for engineering systems in aviation industry. Several approaches for the reliability analysis and their application with examples are presented. It also introduces main trends in the modern reliability theory development.This book will be interested for university students and early-career engineers of aviation industry majors.

This book reflects general trends in the study of geometric aspects of functional analysis, understood in a broad sense. A classical theme in the local theory of Banach spaces is the study of probability measures in high dimension and the concentration of measure phenomenon. Here this phenomenon is approached from different angles, including through analysis on the Hamming cube, and via quantitative estimates in the Central Limit Theorem under thin-shell and related assumptions. Classical convexity theory plays a central role in this volume, as well as the study of geometric inequalities. These inequalities, which are somewhat in spirit of the Brunn-Minkowski inequality, in turn shed light on convexity and on the geometry of Euclidean space. Probability measures with convexity or curvature properties, such as log-concave distributions, occupy an equally central role and arise in the study of Gaussian measures and non-trivial properties of the heat flow in Euclidean spaces. Also discussed are interactions of this circle of ideas with linear programming and sampling algorithms, including the solution of a question in online learning algorithms using a classical convexity construction from the 19th century.

This book provides a compact introduction to the theory of measure-valued branching processes, immigration processes and Ornstein–Uhlenbeck type processes.Measure-valued branching processes arise as high density limits of branching particle systems. The first part of the book gives an analytic construction of a special class of such processes, the Dawson–Watanabe superprocesses, which includes the finite-dimensional continuous-state branching process as an example. Under natural assumptions, it is shown that the superprocesses have Borel right realizations. Transformations are then used to derive the existence and regularity of several different forms of the superprocesses. This technique simplifies the constructions and gives useful new perspectives. Martingale problems of superprocesses are discussed under Feller type assumptions. The second part investigates immigration structures associated with the measure-valued branching processes. The structures are formulated by skewconvolution semigroups, which are characterized in terms of infinitely divisible probability entrance laws. A theory of stochastic equations for one-dimensional continuous-state branching processes with or without immigration is developed, which plays a key role in the construction of measure flows of those processes. The third part of the book studies a class of Ornstein-Uhlenbeck type processes in Hilbert spaces defined by generalized Mehler semigroups, which arise naturally in fluctuation limit theorems of the immigration superprocesses.This volume is aimed at researchers in measure-valued processes, branching processes, stochastic analysis, biological and genetic models, and graduate students in probability theory and stochastic processes.

This book mainly focuses on the sampled-data control of logical networks. We believe that the methods (semi-tensor product of matrices), results (recent results on Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control) and topics (logical networks) in this book have become of particular interest to readers recently. Firstly, logical networks are of interest due to their rich range of applications in biology, game theory, coding, finite automata, graph theory, and other fields. Secondly, semi-tensor product of matrices offers a useful tool for formulating, analyzing and designing controllers for logical networks. Moreover, this book is the first to introduce sampled-data control into the study of logical control networks. All research results in this book are novel and worthy of further study. The book's content is divided into three parts (Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control), which essentially progress from easier to more difficult. In addition, corresponding examples and diagrams are included in each section to facilitate understanding.

This textbook offers an approachable introduction to measure-theoretic probability, illustrating core concepts with examples from statistics and engineering. The author presents complex concepts in a succinct manner, making otherwise intimidating material approachable to undergraduates who are not necessarily studying mathematics as their major. Throughout, readers will learn how probability serves as the language in a variety of exciting fields. Specific applications covered include the coupon collector's problem, Monte Carlo integration in finance, data compression in information theory, and more. Measure-Theoretic Probability is ideal for a one-semester course and will best suit undergraduates studying statistics, data science, financial engineering, and economics who want to understand and apply more advanced ideas from probability to their disciplines. As a concise and rigorous introduction to measure-theoretic probability, it is also suitable for self-study.Prerequisites include a basic knowledge of probability and elementary concepts from real analysis.

This book introduces the basic concepts of set theory, measure theory, the axiomatic theory of probability, random variables and multidimensional random variables, functions of random variables, convergence theorems, laws of large numbers, and fundamental inequalities. The idea is to present a seamless connection between the more abstract advanced set theory, the fundamental concepts from measure theory, and integration, to introduce the axiomatic theory of probability, filling in the gaps from previous books and leading to an interesting, robust and, hopefully, self-contained exposition of the theory.This book also presents an account of the historical evolution of probability theory as a mathematical discipline. Each chapter presents a short biography of the important scientists who helped develop the subject. Appendices include Fourier transforms in one and two dimensions, important formulas and inequalities and commented bibliography. Many examples, illustrations and graphics help the reader understand the theory.

This graduate text presents the elegant and profound theory of continuous parameter Markov processes and many of its applications. The authors focus on developing context and intuition before formalizing the theory of each topic, illustrated with examples.After a review of some background material, the reader is introduced to semigroup theory, including the Hille–Yosida Theorem, used to construct continuous parameter Markov processes. Illustrated with examples, it is a cornerstone of Feller's seminal theory of the most general one-dimensional diffusions studied in a later chapter. This is followed by two chapters with probabilistic constructions of jump Markov processes, and processes with independent increments, or Lévy processes. The greater part of the book is devoted to Itô's fascinating theory of stochastic differential equations, and to the study of asymptotic properties of diffusions in all dimensions, such as explosion, transience, recurrence, existence of steady states, and the speed of convergence to equilibrium. A broadly applicable functional central limit theorem for ergodic Markov processes is presented with important examples. Intimate connections between diffusions and linear second order elliptic and parabolic partial differential equations are laid out in two chapters, and are used for computational purposes. Among Special Topics chapters, two study anomalous diffusions: one on skew Brownian motion, and the other on an intriguing multi-phase homogenization of solute transport in porous media.

Auf dem Weg zu seiner Entscheidungsfindung kann das Gericht an vielen Stellen zweifeln. Dann stellt sich die Frage, wer die Last dieser Zweifel zu tragen hat. Das Strafrecht antwortet darauf grundsätzlich mit in dubio pro reo, im Zweifel für den Angeklagten. Doch noch immer ist der Anwendungsbereich der In-dubio-Regel nicht vollständig geklärt. Das gilt insbesondere für die richterliche Entscheidung unter Anwendung solcher Normen, die dem Gericht ein Wahrscheinlichkeitsurteil abverlangen, also beispielsweise eine Prognose künftiger Ereignisse oder eine Schätzung. Anna Berger untersucht diese Normen genauer und macht ausgehend von der Rechtsnatur der In-dubio-Regel und unter Berücksichtigung der induktiven Logik und der mathematischen Wahrscheinlichkeitstheorie deutlich, dass in dubio pro reo nicht in jeder Situation richterlicher Ungewissheit bei der Anwendung einer Wahrscheinlichkeitsnorm dogmatisch und logisch die korrekte Antwort ist.

With all the insights experienced in the COVID process, one essential remains:'The virus remains a constant companion'. In contrast to regularly occurring infection processes, a COVID infection takes a different course. This is characterized by a dynamic that deviates from conventional, well-known processes in that the originators change their identity and develop corresponding variants. Therefore, preventive infection management - supported by statistical-probabilistic analyzes with PSIR - is important for preventive management of resources and infrastructure for the'waves ahead of the wave'.

Wesentliche Zielsetzung dieses Buchs ist eine in sich abgeschlossene Darstellung der zur Lösung inverser Probleme notwendigen Kenntnisse von der mathematischen Analyse bis zur numerischen Lösung. Konkrete Anwendungsfälle aus Naturwissenschaften und Technik geben den Umfang der benötigten mathematischen Methoden vor. Dazu gehört insbesondere die stochastische Modellierung der unvorhersehbaren Störungen von Messdaten, die bisher in Lehrbüchern zu inversen und schlecht gestellten Problemen nicht berücksichtigt wird. Die stochastische Modellierung steht in engem Zusammenhang mit der für den Computereinsatz essentiellen Diskretisierung beziehungsweise Parametrisierung inverser Probleme, auf die besonderes Augenmerk gerichtet wird. Ein weiterer Schwerpunkt ist die praktische Lösung der aus der Diskretisierung resultierenden globalen, im Allgemeinen nichtlinearen Optimierungsprobleme. Hingegen wird auf die Besprechung einer abstrakten Theorie der Regularisierung verzichtet.Um den ganzen Weg von der theoretischen Analyse bis zur effizienten numerischen Lösung inverser Probleme aufzeigen zu können, wird die Besprechung mathematischer Grundlagen gegenüber Standardtexten um die Einbeziehung von Themen der Wahrscheinlichkeitstheorie und Statistik, der Approximation mit Wavelets und dünnen Gittern sowie der globalen Optimierung wesentlich erweitert.Für eine Reihe von repräsentativen Anwendungsfällen aus den Bereichen Mobilfunk, Medizintechnik oder Geophysik werden die jeweiligen, zumeist nichtlinearen Probleme mathematisch präzisiert, eingehend analysiert und rechnerisch gelöst.Das Buch ist zum Selbststudium für Mathematiker und für mathematisch interessierte Ingenieure und Naturwissenschaftler geeignet.