Stokastik

This book presents in a compact form the program carried out in introductory statistics courses and discusses some essential topics for research activity, such as Monte Carlo simulation techniques, methods of statistical inference, best fit and analysis of laboratory data. All themes are developed starting from fundamentals, highlighting their applicative aspects, up to the detailed description of several cases particularly relevant for technical and scientific research. The text is dedicated to university students in scientific fields and to all researchers who have to solve practical problems by applying data analysis and simulation procedures. The R software is adopted throughout the book, with a rich library of original programs accessible to the readers through a website.

This book gives an introduction to discrete-time Markov chains which evolve on a separable metric space. The focus is on the ergodic properties of such chains, i.e., on their long-term statistical behaviour. Among the main topics are existence and uniqueness of invariant probability measures, irreducibility, recurrence, regularizing properties for Markov kernels, and convergence to equilibrium. These concepts are investigated with tools such as Lyapunov functions, petite and small sets, Doeblin and accessible points, coupling, as well as key notions from classical ergodic theory. The theory is illustrated through several recurring classes of examples, e.g., random contractions, randomly switched vector fields, and stochastic differential equations, the latter providing a bridge to continuous-time Markov processes.  The book can serve as the core for a semester- or year-long graduate course in probability theory with an emphasis on Markov chains or random dynamics. Some of the material is also well suited for an ergodic theory course. Readers should have taken an introductory course on probability theory, based on measure theory. While there is a chapter devoted to chains on a countable state space, a certain familiarity with Markov chains on a finite state space is also recommended.

Dieses Lehrbuch führt in das faszinierende Gebiet der stochastischen Prozesse ein, indem es die entsprechenden Inhalte verständlich darstellt und sie mit Anwendungen aus den Natur- und Ingenieurwissenschaften verbindet. Es enthält zahlreiche vollständig durchgerechnete Beispiele, in denen bei Bedarf die Softwaretools MATLAB und Mathematica eingesetzt werden. Mithilfe von sowohl theoretischen als auch anwendungsorientierten Übungsaufgaben können die vorgestellten Verfahren erlernt und das Verständnis vertieft werden. Für fast alle Aufgaben werden vollständige Lösungswege im Buch oder im zugehörigen YouTube-Kanal der Autoren präsentiert. Zur Aneignung und Festigung der Inhalte erhalten Leser des Buchs zudem passende Lernfragen, auf die sie in der Springer-Flashcards-App zugreifen können. Das Buch bietet somit ein stimmiges Gesamtpaket und eignet sich hervorragend zum Selbststudium.Nach einem Überblick über die notwendigen mathematischen Grundlagen erfolgt eine ausführliche und mit vielen Beispielen gestützte Einführung in Theorie und Praxis zeitdiskreter und zeitstetiger Markoff-Ketten, sowie Varianten von Markoff-Prozessen ¿ insbesondere Wiener-Prozesse zur Modellierung der Brown¿schen Bewegung. Danach erfolgen systematische Einführungen in Martingale, Warteschlangen und die Zuverlässigkeitstheorie. Monte-Carlo-Simulationen finden in einem eigenen Kapitel Platz, das neben einer ausführlichen Beschreibung der Methode auch Spielraum für eigene Experimente mit den dort vorgestellten Programmen bietet. Eine Einführung in die Grundlagen der (stochastischen) Petri-Netze und ein leicht zugänglicher Einstieg in die stochastische Analysis (stochastische Integrale, stochastische Differentialgleichungen) runden das Buch ab.

Im Mittelpunkt dieses essentials steht eine Einfuhrung in ein bekanntes statistisches Modell, das Hidden-Markov-Modell.Damit konnen Probleme bewaltigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll.Die Anwendungen des Hidden-Markov-Modells liegen hauptsachlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung.In diesem Buchlein werden die beiden zentralen Problemstellungen in HMMs behandelt.Das Problem der Inferenz wird mit dem beruhmten Viterbi-Algorithmus gelost, und das Problem der Parameterschatzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).

This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.

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.

Wahrend in den experimentellen Wissenschaften fast ausschlielich von statistischer Wahrscheinlichkeit die Rede ist, verstehen in der Philosophie einflussreiche Bayesianer Wahrscheinlichkeit durchweg im subjektiven Sinn rationaler Glaubensgrade, wogegen die dritte Gruppe der mathematischen Wahrscheinlichkeitstheoretiker diesen Interpretationskonflikt ignoriert. In diesem Buch wird die Auffassung vertreten, dass man beide Wahrscheinlichkeitsbegriffe benotigt, weshalb ein dualistischer Ansatz entwickelt wird, dem es darum geht, Bruckenprinzipien zwischen beiden Wahrscheinlichkeitsbegriffen herauszuarbeiten. In Anlehnung an einen bekannten Passus von Kant lasst sich die dualistische Position so formulieren: Subjektive ohne statistische Wahrscheinlichkeitstheorie ist blind, statistische ohne subjektive Wahrscheinlichkeitstheorie ist leer. Die dualistische Position bedeutet jedoch nicht, dass alles, was in beiden Positionen behauptet wurde, ubernommen werden kann; dies wurde zu Widerspruchen fuhren. In beiden Positionen mussen gewisse Anteile fallen gelassen werden, um zu einer koharenten dualistischen Wahrscheinlichkeitstheorie zu gelangen.

This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022.The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition.The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

This book provides the foundations for geometric applications of convex cones and presents selected examples from a wide range of topics, including polytope theory, stochastic geometry, and Brunn-Minkowski theory. Giving an introduction to convex cones, it describes their most important geometric functionals, such as conic intrinsic volumes and Grassmann angles, and develops general versions of the relevant formulas, namely the Steiner formula and kinematic formula. In recent years questions related to convex cones have arisen in applied mathematics, involving, for example, properties of random cones and their non-trivial intersections. The prerequisites for this work, such as integral geometric formulas and results on conic intrinsic volumes, were previously scattered throughout the literature, but no coherent presentation was available. The present book closes this gap. It includes several pearls from the theory of convex cones, which should be better known. 

This book puts forward a new mathematical theory to study chaotic phenomenon. The uniform theory is established on the basis of two elementary concept of circle and externally tangent square in mathematics. The author studies the uniformity of a finite set of points distributed in space by uniform theory. This book also illustrates that uniform theory performs better than other indices such as entropy and Lyapunov exponent in chaos measurement by numerous examples. This book develops a new mathematical tool for studying chaos so it will be appealing to students and researchers interested in theory of chaos. It also has potential applications in various fields such as Engineering, Forestry and Ecology.

This textbook, now in its second revised and extended edition, presents the fundamental ideas and results of both probability theory and statistics. It comprises the material of a one-year course, which is addressed to students of mathematics and to scientists with an interest in the mathematical side of stochastics.The stochastic concepts, models and methods are motivated by examples and then developed and analysed systematically. Some measure theory is included, but this is done at an elementary level that is in accordance with the introductory character of the book. A large number of problems, now in part with solutions, offer applications and supplements to the text.

This book covers a wide range of problems involving the applications of stochastic processes, stochastic calculus, large deviation theory, group representation theory and quantum statistics.

This book deals with certain important problems in Classical and Quantum Information Theory and will be very helpful for students of Undergraduate and Postgraduate Courses in Electronics, Communication and Signal Processing.

This book provides analytic tools to describe local and global behavior of solutions to Ito-stochastic differential equations with non-degenerate Sobolev diffusion coefficients and locally integrable drift. Regularity theory of partial differential equations is applied to construct such solutions and to obtain strong Feller properties, irreducibility, Krylov-type estimates, moment inequalities, various types of non-explosion criteria, and long time behavior, e.g., transience, recurrence, and convergence to stationarity. The approach is based on the realization of the transition semigroup associated with the solution of a stochastic differential equation as a strongly continuous semigroup in the Lp-space with respect to a weight that plays the role of a sub-stationary or stationary density. This way we obtain in particular a rigorous functional analytic description of the generator of the solution of a stochastic differential equation and its full domain. The existence of such a weight is shown under broad assumptions on the coefficients. A remarkable fact is that although the weight may not be unique, many important results are independent of it. Given such a weight and semigroup, one can construct and further analyze in detail a weak solution to the stochastic differential equation combining variational techniques, regularity theory for partial differential equations, potential, and generalized Dirichlet form theory. Under classical-like or various other criteria for non-explosion we obtain as one of our main applications the existence of a pathwise unique and strong solution with an infinite lifetime. These results substantially supplement the classical case of locally Lipschitz or monotone coefficients.We further treat other types of uniqueness and non-uniqueness questions, such as uniqueness and non-uniqueness of the mentioned weights and uniqueness in law, in a certain sense, of the solution.