This monograph addresses the problem of "real-time" curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except for a vector parameter. In contrast to the traditional formulation, data are imagined to arrive in temporal succession. The estimation is carried out in real time so that, at each instant, the parameter estimate fully reflects all available data.Specifically, the monograph focuses on estimator sequences of the so-called differential correction type. The term "differential correction" refers to the fact that the difference between the components of the updated and previous estimators is proportional to the difference between the current observation and the value that would be predicted by the regression function if the previous estimate were in fact the true value of the unknown vector parameter. The vector of proportionality factors (which is generally time varying and can depend upon previous estimates) is called the "gain" or "smoothing" vector.The main purpose of this research is to relate the large-sample statistical behavior of such estimates (consistency, rate of convergence, large-sample distribution theory, asymptotic efficiency) to the properties of the regression function and the choice of smoothing vectors. Furthermore, consideration is given to the tradeoff that can be effected between computational simplicity and statistical efficiency through the choice of gains.Part I deals with the special cases of an unknown scalar parameter-discussing probability-one and mean-square convergence, rates of mean-square convergence, and asymptotic distribution theory of the estimators for various choices of the smoothing sequence. Part II examines the probability-one and mean-square convergence of the estimators in the vector case for various choices of smoothing vectors. Examples are liberally sprinkled throughout the book. Indeed, the last chapter is devoted entirely to the discussion of examples at varying levels of generality.If one views the stochastic approximation literature as a study in the asymptotic behavior of solutions to a certain class of nonlinear first-order difference equations with stochastic driving terms, then the results of this monograph also serve to extend and complement many of the results in that literature, which accounts for the authors' choice of title.The book is written at the first-year graduate level, although this level of maturity is not required uniformly. Certainly the reader should understand the concept of a limit both in the deterministic and probabilistic senses (i.e., almost sure and quadratic mean convergence). This much will assure a comfortable journey through the first fourth of the book. Chapters 4 and 5 require an acquaintance with a few selected central limit theorems. A familiarity with the standard techniques of large-sample theory will also prove useful but is not essential. Part II, Chapters 6 through 9, is couched in the language of matrix algebra, but none of the "classical" results used are deep. The reader who appreciates the elementary properties of eigenvalues, eigenvectors, and matrix norms will feel at home.MIT Press Research Monograph No. 42

The computer age has spawned a whole new discipline of Computational Physics, a branch of physics known for its dynamic nature as opposed to the more traditional branches of experimental and theoretical physics. Within the field, the topic that has "inflated" the most with the rise of the computer is that of Stochastic Simulation, more colourfully known by its distinguished proponents, Fermi, von Neumann, Metropolis, Ulam and others such as the Monte Carlo method. Kevin MacKeown's book, the ideas of which had evolved from 15 years of teaching a final year undergraduate course on Computational Physics, serves to summarise in one volume the past and latest developments of the stochastic phenomena in the context of physics. The "teaching" approach follows a less conventional one in that there is no canon to be followed in the field. Instead, the topics are chosen to give a feeling for the breadth of applications of Monte Carlo methods in physics. Substantial references to research literature are also provided. This book is an essential reference to students wishing to gain a more technical interest in the subject as a way of getting quatitative answers to a problem. The level of knowledge of physics assumed corresponds to a that of a final year undergraduate student, but postgraduate students in a number of disciplines should also find the material of value.

Stochastic Processes and Random Vibrations Theory and Practice JAlA-us SA3lnes University of Iceland, ReykjavA-k, Iceland This book covers the fundamental theory of stochastic processes for analysing mechanical and structural systems subject to random excitation, and also for treating random signals of a general nature with special emphasis on earthquakes and turbulent winds. Starting with basic probability calculus and the fundamental theory of stochastic processes, the author progresses onto engineering applications: systems analysis and treatment of random signals. The random excitation and response of simple mechanical systems and complex structural systems is discussed in some detail. Extreme conditions such as distribution of large vibration peaks, random excursions above certain limits and mechanical failure due to fatigue are then addressed. The text also offers a discussion of some well-known stochastic models and an introduction to signal processing and digital filters. Numerous worked examples are included: distribution of extreme wind speeds, analysis of structural reliability, earthquake response of a tall multi-storey structure, wind loading of tall towers, generation of random earthquake signals and earthquake risk analysis.

This text develops an introductory and relatively simple account of the theory and application of the evolutionary type of stochastic process. Professor Bailey adopts the heuristic approach of applied mathematics and develops both theoretical principles and applied techniques simultaneously.

The Wiley Classics Library consists of selected books originally published by John Wiley & Sons that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes With Applications to the Natural Sciences R. W. Carter Simple Groups of Lie Type Richard Courant Differential and Integral Calculus. Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II Harold S.M. Coxeter Introduction to Modern Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups and Associative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory With Applications to Finite Groups and Orders, Volume 1 W. Edwards Darning Sample Design in Business Research Amos deShalit & Herman Feshbach Theoretical Nuclear Physics, Volume I-Nuclear Structure J. L. Doob Stochastic Processes Nelson Dunford, Jacob T. Schwartz Linear Operators, Part One, General Theory Nelson Dunford, Jacob T. Schwartz Linear Operators, Part Two, Spectral Theory - Self Adjoint Operators in Hilbert Space Nelson Dunford, Jacob T. Schwartz Linear Operators, Part Three, Spectral Operators Peter Henrici Applied and Computational Complex Analysis, Volume I - Power Series-Integration-Conformal Mapping-Location of Zeros Peter Hilton, Yel-Chiang Wu A Course in Modern Algebra Harry Hochstadt Integral Equations Erwin Kreyszig Introductory Functional Analysis with Applications William H. Louisell Quantum Statistical Properties of Radiation P. M. Prenter Splines and Variational Methods Walter Rudin Fourier Analysis on Groups C. L. Siegel Topics in Complex Function Theory Volume I - Elliptic Functions and Uniformization Theory C. L. Siegel Topics in Complex Function Theory Volume II - Automorphic and Abelian Integrals C. L. Siegel Topics in Complex Function Theory, Volume III - Abelian Functions & Modular Functions of Several Variables J. J. Stoker Differential Geometry

Dieses vierfarbige Lehrbuch wendet sich an Student(inn)en der Mathematik in Bachelor-Studiengangen. Es bietet eine fundierte, lebendige und mit diversen Erklarvideos audiovisuell erweiterte Einfuhrung sowohl in die Stochastik einschliesslich der Mathematischen Statistik als auch der Mass- und Integrationstheorie. Durch besondere didaktische Elemente eignet es sich insbesondere zum Selbststudium und als vorlesungsbegleitender Text. Herausragende Merkmale sind: durchgangig vierfarbiges Layout mit mehr als 140 Abbildungen pragnant formulierte Kerngedanken bilden die Abschnittsuberschriften Selbsttests ermoeglichen Lernkontrollen wahrend des Lesens farbige Merkkasten heben das Wichtigste hervor "Unter-der-Lupe"-Boxen zoomen in Beweise hinein, motivieren und erklaren Details "Hintergrund-und-Ausblick"-Boxen betrachten weiterfuhrende Gesichtspunkte Zusammenfassungen zu jedem Kapitel sowie UEbersichtsboxen mehr als 330 UEbungsaufgaben zahlreiche uber QR-Codes verlinkte Erklarvideos Die Inhalte dieses Buches basieren groesstenteils auf dem Werk "Grundwissen Mathematikstudium - Hoehere Analysis, Numerik und Stochastik", werden aber wegen der curricularen Bedeutung hiermit in vollstandig uberarbeiteter Form als eigenstandiges Werk veroeffentlicht.

Dieses Buch verschafft Ihnen einen UEberblick uber einige der bekanntesten Verfahren des maschinellen Lernens aus der Perspektive der mathematischen Statistik. Nach der Lekture kennen Sie die jeweils gestellten Forderungen an die Daten sowie deren Vor- und Nachteile und sind daher in der Lage, fur ein gegebenes Problem ein geeignetes Verfahren vorzuschlagen. Beweise werden nur dort ausfuhrlich dargestellt oder skizziert, wo sie einen didaktischen Mehrwert bieten - ansonsten wird auf die entsprechenden Fachartikel verwiesen. Fur die praktische Anwendung ist ein genaueres Studium des jeweiligen Verfahrens und der entsprechenden Fachliteratur noetig, zu der Sie auf Basis dieses Buchs aber schnell Zugang finden. Das Buch richtet sich an Studierende der Mathematik hoeheren Semesters, die bereits Vorkenntnisse in Wahrscheinlichkeitstheorie besitzen. Behandelt werden sowohl Methoden des Supervised Learning und Reinforcement Learning als auch des Unsupervised Learning. Der Umfang entspricht einer einsemestrigen vierstundigen Vorlesung. Die einzelnen Kapitel sind weitestgehend unabhangig voneinander lesbar, am Ende jedes Kapitels kann das erworbene Wissen anhand von UEbungsaufgaben und durch Implementierung der Verfahren uberpruft werden. Quelltexte in der Programmiersprache R stehen auf der Springer-Produktseite zum Buch zur Verfugung.

Das Lehrbuch vermittelt solides Basiswissen zu den thematischen Schwerpunkten Produktmasse, Fourier-Transformation, Transformationsformel, Konvergenzbegriffe, absolute Stetigkeit und Masse auf topologischen Raumen. Hoehepunkte sind die Herleitung des Riesz'schen Darstellungssatzes und der Beweis der Existenz und Eindeutigkeit des Haar'schen Masses. Der Band enthalt ferner mathematikhistorische Ausfluge und Kurzportrats von Mathematikern, die zum Thema des Buchs wichtige Beitrage geliefert haben, sowie zahlreiche UEbungsaufgaben zur Vertiefung des Stoffs.

Dieses Buch ist eine Einfuhrung in die Variationsrechnung, die das Ziel hat, reellwertige Funktionale zu minimieren oder zu maximieren. Die Funktionale sind Integrale uber einem Intervall, weshalb die dafur zulassigen Funktionen von nur einer unabhangigen Variablen abhangen. Motiviert werden die Fragestellungen durch viele und zum Teil auch historisch bedeutsame Beispiele.
Die Theorie fuhrt in den Euler-Lagrange-Kalkul und in die Direkten Methoden der Variationsrechnung ein. Die Ausfuhrungen werden von Abbildungen begleitet, die das Verstandnis erleichtern. Zu jedem Abschnitt werden Ubungsaufgaben gestellt, deren Losungen am Ende des Buches zu finden sind.
Das Buch ist fur Vorlesungen ab dem 3. Semester geeignet. Die Hilfsmittel, welche uber die der Grundvorlesungen hinausgehen, werden im Text oder im Anhang bereitgestellt.
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Aufbauend auf dem ersten Band, werden in diesem Buch weiterfuhrende Konzepte der Wahrscheinlichkeitstheorie ausfuhrlich und verstandlich diskutiert. Mit vielen exemplarisch durchgerechneten Aufgaben, einer Vielzahl weiterer Problemstellungen und ausfuhrlichen Loesungen bietet es dem Leser die Moeglichkeit, die eigenen Fahigkeiten standig zu erweitern und kritisch zu uberprufen und ein tieferes Verstandnis der Materie zu erlangen. Realitatsnahe Anwendungen ermoeglichen einen Ausblick in die breite Verwendbarkeit dieser Theorie.Auch in diesem Band wird auf die Entwicklung der Begriffsbildung und der mathematischen Konzepte besonderer Wert gelegt, sodass man ihre Bedeutung bei der Erzeugung wie auch standige Verbesserung von Forschungsinstrumenten fur die Untersuchung unserer Welt erleben kann. Gerichtet ist das Buch an Gymnasiasten, Studienanfanger an Hochschulen, Lehrer und Interessierte, die sich mit diesem Gebiet vertraut machen moechten.

Probability comes of age with this, the first dictionary of probability and its applications in English, which supplies a guide to the concepts and vocabulary of this rapidly expanding field. Besides the basic theory of probability and random processes, applications covered here include financial and insurance mathematics, operations research (including queueing, reliability, and inventories), decision and game theory, optimization, time series, networks, and communication theory, as well as classic problems and paradoxes. The dictionary is reliable, stable, concise, and cohesive. Each entry provides a rigorous definition, a sketch of the context, and a reference pointing the reader to the wider literature. Judicious use of figures makes complex concepts easier to follow without oversimplifying. As the only dictionary on the market, this will be a guiding reference for all those working in, or learning, probability together with its applications.

In many real-world applications, the problems with the data used for scheduling such as processing times, set-up times, release dates or due dates is not exactly known before applying a specific solution algorithm which restricts practical aspects of scheduling theory. During the last decades, several approaches have been developed for sequencing and scheduling with inaccurate data, depending on whether the data is given as random numbers, fuzzy numbers or whether it is uncertain (ie: it can take values from a given interval). This book considers the four major approaches for dealing with such problems: a stochastic approach, a fuzzy approach, a robust approach and a stability approach. Each of the four parts is devoted to one of these approaches. First, it contains a survey chapter on this subject, as well as between further chapters, presenting some recent research results in the particular area. The book provides the reader with a comprehensive and up-to-date introduction into scheduling with inaccurate data. The four survey chapters deal with scheduling with stochastic approaches, fuzzy job-shop scheduling, min-max regret scheduling problems and a stability approach to sequencing and scheduling under uncertainty. This book will be useful for applied mathematicians, students and PhD students dealing with scheduling theory, optimisation and calendar planning.

The stability of stochastic differential equations in abstract, mainly Hilbert, spaces receives a unified treatment in this self-contained book. It covers basic theory as well as computational techniques for handling the stochastic stability of systems from mathematical, physical and biological problems. Its core material is divided into three parts devoted respectively to the stochastic stability of linear systems, non-linear systems, and time-delay systems. The focus is on stability of stochastic dynamical processes affected by white noise, which are described by partial differential equations such as the Navier-Stokes equations. A range of mathematicians and scientists, including those involved in numerical computation, will find this book useful. It is also ideal for engineers working on stochastic systems and their control, and researchers in mathematical physics or biology.

Probability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains. A special feature is the authors' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers and the central limit theorem. There is an account of moment generating functions and their applications. The following three chapters are about branching processes, random walks, and continuous-time random processes such as the Poisson process. The final chapter is a fairly extensive account of Markov chains in discrete time. This second edition develops the success of the first edition through an updated presentation, the extensive new chapter on Markov chains, and a number of new sections to ensure comprehensive coverage of the syllabi at major universities.

This book develops the theory of continuous and discrete stochastic processes within the context of cell biology. In the second edition the material has been significantly expanded, particularly within the context of nonequilibrium and self-organizing systems. Given the amount of additional material, the book has been divided into two volumes, with volume I mainly covering molecular processes and volume II focusing on cellular processes. A wide range of biological topics are covered in the new edition, including stochastic ion channels and excitable systems, molecular motors, stochastic gene networks, genetic switches and oscillators, epigenetics, normal and anomalous diffusion in complex cellular environments, stochastically-gated diffusion, active intracellular transport, signal transduction, cell sensing, bacterial chemotaxis, intracellular pattern formation, cell polarization, cell mechanics, biological polymers and membranes, nuclear structure and dynamics, biological condensates, molecular aggregation and nucleation, cellular length control, cell mitosis, cell motility, cell adhesion, cytoneme-based morphogenesis, bacterial growth, and quorum sensing. The book also provides a pedagogical introduction to the theory of stochastic and nonequilibrium processes - Fokker Planck equations, stochastic differential equations, stochastic calculus, master equations and jump Markov processes, birth-death processes, Poisson processes, first passage time problems, stochastic hybrid systems, queuing and renewal theory, narrow capture and escape, extreme statistics, search processes and stochastic resetting, exclusion processes, WKB methods, large deviation theory, path integrals, martingales and branching processes, numerical methods, linear response theory, phase separation, fluctuation-dissipation theorems, age-structured models, and statistical field theory. This text is primarily aimed at graduate students and researchers working in mathematical biology, statistical and biological physicists, and applied mathematicians interested in stochastic modeling. Applied probabilists should also find it of interest. It provides significant background material in applied mathematics and statistical physics, and introduces concepts in stochastic and nonequilibrium processes via motivating biological applications. The book is highly illustrated and contains a large number of examples and exercises that further develop the models and ideas in the body of the text. It is based on a course that the author has taught at the University of Utah for many years.

We propose results of the investigation of the problem of mean square optimal estimation of linear functionals constructed from unobserved values of stationary stochastic processes. Estimates are based on observations of the processes with additive stationary noise process. The aim of the book is to develop methods for finding the optimal estimates of the functionals in the case where some observations are missing. Formulas for computing values of the mean-square errors and the spectral characteristics of the optimal linear estimates of functionals are derived in the case of spectral certainty, where the spectral densities of the processes are exactly known. The minimax robust method of estimation is applied in the case of spectral uncertainty, where the spectral densities of the processes are not known exactly while some classes of admissible spectral densities are given. The formulas that determine the least favourable spectral densities and the minimax spectral characteristics of the optimal estimates of functionals are proposed for some special classes of admissible densities.

This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense of local asymptotic (mixed) normality or local asymptotic quadraticity make up the core of the book. Numerous examples deal with classical independent and identically distributed models and with stochastic processes. The book can be read in different ways, according to possibly different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift models, mixed normal and quadratic models, and on local asymptotics where the limit model is a Gaussian shift or a mixed normal or a quadratic experiment (LAN, LAMN, LAQ). Another reader may prefer an introduction to stochastic process models where given statistical results apply, and thus concentrate on subsections or chapters on likelihood ratio processes and some diffusion type models where LAN, LAMN or LAQ occurs. Finally, readers might put together both aspects. The book is suitable for graduate students starting to work in statistics of stochastic processes, as well as for researchers interested in a precise introduction to this area.

Stochastic calculus and stochastic differential equations play an assertive role in many applications including physics, biology, financial and actuarial modelling. Well known phenomena have been described in the past by deterministic differential equations. Due to the presence of indeterminate factors, the same phenomena can be better modelled by stochastic equations. Therefore, stochastic differential equations are more realistic to the real world than the deterministic ones. This book examines new results from different fields of interest in the wide area of stochastic differential equations and their applications.

Stochastic scheduling is in the area of production scheduling. There is a dearth of work that analyzes the variability of schedules. In a stochastic environment, in which the processing time of a job is not known with certainty, a schedule is typically analyzed based on the expected value of a performance measure. This book addresses this problem and presents algorithms to determine the variability of a schedule under various machine configurations and objective functions. It is intended for graduate and advanced undergraduate students in manufacturing, operations management, applied mathematics, and computer science, and it is also a good reference book for practitioners. Computer software containing the algorithms is provided on an accompanying Web site for ease of student and user implementation.

This monograph presents important research results in the areas of queuing theory, risk theory, graph theory and reliability theory. The analysed stochastic network models are aggregated systems of elements in random environments. To construct and to analyse a large number of different stochastic network models it is possible by a proof of new analytical results and a construction of calculation algorithms besides of the application of cumbersome traditional techniques Such a constructive approach is in a prior detailed investigation of an algebraic model component and leads to an appearance of new original stochastic network models, algorithms and application to computer science and information technologies. Accuracy and asymptotic formulas, additional calculation algorithms have been constructed due to an introduction of control parameters into analysed models, a reduction of multi-dimensional problems to one dimensional problems, a comparative analysis, a graphic interpretation of network models, an investigation of new models characteristics, a choice of special distributions classes or principles of subsystems aggregation, proves of new statements.

This book is a translation of the third edition of the well accepted German textbook 'Stochastik', which presents the fundamental ideas and results of both probability theory and statistics, and comprises the material of a one-year course. The stochastic concepts, models and methods are motivated by examples and problems and then developed and analysed systematically.

This book presents both the fundamentals and the major research topics in statistical physics of systems out of equilibrium. It summarizes different approaches to describe such systems on the thermodynamic and stochastic levels, and discusses a variety of areas including reactions, anomalous kinetics, and the behavior of self-propelling particles.

The monograph is devoted to an investigation of co-operative effects in stochastic models. It includes original results of the authors in the last decade. The main object of the monograph is an analysis of an influence of a stochastic model structure on its characteristics. Problems of a co-operation and a decomposition are actual in a solution of a lot of concrete problems. These problems are: a parallelisation of algorithms and programs, a modelling of supercomputers, computer networks, systems of mobile telephones catastrophes in complex systems, a design and an improvement of technological and economical processes etc. The co-operative effects create a source of significant dependencies between complex system characteristics under large random disturbances. To analyse these effects is necessary to create special methods based on structural analysis of multi-element stochastic models together with majoral asymptotic bounds of these models characteristics. At the same time it demands to develop new approaches to a processing of statistical data and a skill in an usage of the probability theory limit theorems and related asymptotic series and bounds. A choice of the monograph material is defined as by initial applied problems so by probability methods of their solution. Conditionally the monograph may be divided into two parts. First of them contains four sections devoted to a finding of the co-operative effects and to a development of new related analytical and numerical methods. This part has presumably methodological character and creates a theoretical base of an investigation of applied stochastic systems. Second part contains three sections devoted to a solution of different applied problems. It has some interesting substantial results.

From the ancients' first readings of the innards of birds to your neighbor's last bout with the state lottery, humankind has put itself into the hands of chance. Today life itself may be at stake when probability comes into play--in the chance of a false negative in a medical test, in the reliability of DNA findings as legal evidence, or in the likelihood of passing on a deadly congenital disease--yet as few people as ever understand the odds. This book is aimed at the trouble with trying to learn about probability. A story of the misconceptions and difficulties civilization overcame in progressing toward probabilistic thinking, Randomness is also a skillful account of what makes the science of probability so daunting in our own day. To acquire a (correct) intuition of chance is not easy to begin with, and moving from an intuitive sense to a formal notion of probability presents further problems. Author Deborah Bennett traces the path this process takes in an individual trying to come to grips with concepts of uncertainty and fairness, and also charts the parallel path by which societies have developed ideas about chance. Why, from ancient to modern times, have people resorted to chance in making decisions? Is a decision made by random choice "fair"? What role has gambling played in our understanding of chance? Why do some individuals and societies refuse to accept randomness at all? If understanding randomness is so important to probabilistic thinking, why do the experts disagree about what it really is? And why are our intuitions about chance almost always dead wrong? Anyone who has puzzled over a probability conundrum is struck by the paradoxes and counterintuitive results that occur at a relatively simple level. Why this should be, and how it has been the case through the ages, for bumblers and brilliant mathematicians alike, is the entertaining and enlightening lesson of Randomness.