An Introduction to Stochastic Orders


Author: Felix Belzunce,Carolina Martinez Riquelme,Julio Mulero
Publisher: Academic Press
ISBN: 0128038268
Category: Mathematics
Page: 174
View: 3739
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An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different areas such as reliability, survival analysis, risks, finance, and economics. The book provides a general background on this topic for students and researchers who want to use it as a tool for their research. In addition, users will find detailed proofs of the main results and applications to several probabilistic models of interest in several fields, and discussions of fundamental properties of several stochastic orders, in the univariate and multivariate cases, along with applications to probabilistic models. Introduces stochastic orders and its notation Discusses different orders of univariate stochastic orders Explains multivariate stochastic orders and their convex, likelihood ratio, and dispersive orders

Introduction to Stochastic Processes with R


Author: Robert P. Dobrow
Publisher: John Wiley & Sons
ISBN: 1118740653
Category: Mathematics
Page: 504
View: 5460
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An introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical freeware R, makes theoretical results come alive with practical, hands-on demonstrations. Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers’ problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features: Over 200 examples and 600 end-of-chapter exercises A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra Discussions of many timely and interesting supplemental topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black-Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus Introductions to mathematics as needed in order to suit readers at many mathematical levels A companion website that includes relevant data files as well as all R code and scripts used throughout the book Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.

An Introduction to Probability and Stochastic Processes


Author: James L. Melsa,Andrew P. Sage
Publisher: Courier Corporation
ISBN: 0486490998
Category: Mathematics
Page: 403
View: 5254
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Detailed coverage of probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.

Introduction to Stochastic Processes


Author: Paul G. Hoel,Sidney C. Port,Charles Joël Stone
Publisher: Houghton Mifflin Harcourt (HMH)
ISBN: N.A
Category: Mathematics
Page: 203
View: 7045
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Markov chains; Stationary distributions of a markov chain; Markov pure jump processes; Second order processes; Continuity, integration, and differentiation of second order processes; Stochastic differential equations, estimation theory, and spectral distribution.

Stochastic Differential Equations

An Introduction with Applications
Author: Bernt Øksendal
Publisher: Springer Science & Business Media
ISBN: 3642143946
Category: Mathematics
Page: 379
View: 5840
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This book gives an introduction to the basic theory of stochastic calculus and its applications. Examples are given throughout the text, in order to motivate and illustrate the theory and show its importance for many applications in e.g. economics, biology and physics. The basic idea of the presentation is to start from some basic results (without proofs) of the easier cases and develop the theory from there, and to concentrate on the proofs of the easier case (which nevertheless are often sufficiently general for many purposes) in order to be able to reach quickly the parts of the theory which is most important for the applications. For the 6th edition the author has added further exercises and, for the first time, solutions to many of the exercises are provided. Apart from several minor corrections and improvements, based on useful comments from readers and experts, the most important change in the corrected 5th printing of the 6th edition is in Theorem 10.1.9, where the proof of part b has been corrected and rewritten. The corrected 5th printing of the 6th edition is forthcoming and expected in September 2010.

Introduction to Probability with R


Author: Kenneth Baclawski
Publisher: CRC Press
ISBN: 9781420065220
Category: Mathematics
Page: 384
View: 7859
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Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. Although the R programs are small in length, they are just as sophisticated and powerful as longer programs in other languages. This brevity makes it easy for students to become proficient in R. This calculus-based introduction organizes the material around key themes. One of the most important themes centers on viewing probability as a way to look at the world, helping students think and reason probabilistically. The text also shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. In addition, it presents a unified treatment of transforms, such as Laplace, Fourier, and z; the foundations of fundamental stochastic processes using entropy and information; and an introduction to Markov chains from various viewpoints. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers. The book has an accompanying website with more information.

An Introduction to Stochastic Process


Author: Adhir K. Basu
Publisher: CRC Press
ISBN: 9780849309915
Category: Mathematics
Page: 224
View: 1879
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Designed for college mathematics students at all levels, this book grew from the author's lectures for advanced undergraduate courses at Canadian and United States universities, and from a postgraduate course at Calcutta University. It introduces discrete time Markov chain and second order stochastic analysis, and includes discussions of renewal theory, time series analysis, queuing theory, Brownian motions, and martingale theorems.

An Introduction to Mathematical Analysis for Economic Theory and Econometrics


Author: Dean Corbae,Maxwell B. Stinchcombe,Juraj Zeman
Publisher: Princeton University Press
ISBN: 1400833086
Category: Business & Economics
Page: 688
View: 1656
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Providing an introduction to mathematical analysis as it applies to economic theory and econometrics, this book bridges the gap that has separated the teaching of basic mathematics for economics and the increasingly advanced mathematics demanded in economics research today. Dean Corbae, Maxwell B. Stinchcombe, and Juraj Zeman equip students with the knowledge of real and functional analysis and measure theory they need to read and do research in economic and econometric theory. Unlike other mathematics textbooks for economics, An Introduction to Mathematical Analysis for Economic Theory and Econometrics takes a unified approach to understanding basic and advanced spaces through the application of the Metric Completion Theorem. This is the concept by which, for example, the real numbers complete the rational numbers and measure spaces complete fields of measurable sets. Another of the book's unique features is its concentration on the mathematical foundations of econometrics. To illustrate difficult concepts, the authors use simple examples drawn from economic theory and econometrics. Accessible and rigorous, the book is self-contained, providing proofs of theorems and assuming only an undergraduate background in calculus and linear algebra. Begins with mathematical analysis and economic examples accessible to advanced undergraduates in order to build intuition for more complex analysis used by graduate students and researchers Takes a unified approach to understanding basic and advanced spaces of numbers through application of the Metric Completion Theorem Focuses on examples from econometrics to explain topics in measure theory

An Introduction to Stochastic Modeling


Author: Howard M. Taylor,Samuel Karlin
Publisher: Academic Press
ISBN: 1483269272
Category: Mathematics
Page: 410
View: 7786
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An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

An Introduction to Sparse Stochastic Processes


Author: Michael Unser,Pouya D. Tafti
Publisher: Cambridge University Press
ISBN: 1316061604
Category: Technology & Engineering
Page: N.A
View: 4168
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Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.

Introduction to Stochastic Calculus Applied to Finance, Second Edition


Author: Damien Lamberton,Bernard Lapeyre
Publisher: CRC Press
ISBN: 142000994X
Category: Mathematics
Page: 254
View: 7613
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Since the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field. New to the Second Edition Complements on discrete models, including Rogers' approach to the fundamental theorem of asset pricing and super-replication in incomplete markets Discussions on local volatility, Dupire's formula, the change of numéraire techniques, forward measures, and the forward Libor model A new chapter on credit risk modeling An extension of the chapter on simulation with numerical experiments that illustrate variance reduction techniques and hedging strategies Additional exercises and problems Providing all of the necessary stochastic calculus theory, the authors cover many key finance topics, including martingales, arbitrage, option pricing, American and European options, the Black-Scholes model, optimal hedging, and the computer simulation of financial models. They succeed in producing a solid introduction to stochastic approaches used in the financial world.

An Introduction to Order Statistics


Author: Mohammad Ahsanullah,Valery B Nevzorov,Mohammad Shakil
Publisher: Springer Science & Business Media
ISBN: 949121683X
Category: Mathematics
Page: 244
View: 638
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This book presents the theory of order statistics in a way, such that beginners can get easily acquainted with the very basis of the theory without having to work through heavily involved techniques. At the same time more experienced readers can check their level of understanding and polish their knowledge with certain details. This is achieved by, on the one hand, stating the basic formulae and providing many useful examples to illustrate the theoretical statements, while on the other hand an upgraded list of references will make it easier to gain insight into more specialized results. Thus this book is suitable for a readership working in statistics, actuarial mathematics, reliability engineering, meteorology, hydrology, business economics, sports analysis and many more.

An Introduction to Quantum Stochastic Calculus


Author: K.R. Parthasarathy
Publisher: Springer Science & Business Media
ISBN: 3034805667
Category: Mathematics
Page: 290
View: 2641
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An Introduction to Quantum Stochastic Calculus aims to deepen our understanding of the dynamics of systems subject to the laws of chance both from the classical and the quantum points of view and stimulate further research in their unification. This is probably the first systematic attempt to weave classical probability theory into the quantum framework and provides a wealth of interesting features: The origin of Ito’s correction formulae for Brownian motion and the Poisson process can be traced to commutation relations or, equivalently, the uncertainty principle. Quantum stochastic integration enables the possibility of seeing new relationships between fermion and boson fields. Many quantum dynamical semigroups as well as classical Markov semigroups are realised through unitary operator evolutions. The text is almost self-contained and requires only an elementary knowledge of operator theory and probability theory at the graduate level. - - - This is an excellent volume which will be a valuable companion both to those who are already active in the field and those who are new to it. Furthermore there are a large number of stimulating exercises scattered through the text which will be invaluable to students. (Mathematical Reviews) This monograph gives a systematic and self-contained introduction to the Fock space quantum stochastic calculus in its basic form (...) by making emphasis on the mathematical aspects of quantum formalism and its connections with classical probability and by extensive presentation of carefully selected functional analytic material. This makes the book very convenient for a reader with the probability-theoretic orientation, wishing to make acquaintance with wonders of the noncommutative probability, and, more specifcally, for a mathematics student studying this field. (Zentralblatt MATH) Elegantly written, with obvious appreciation for fine points of higher mathematics (...) most notable is [the] author's effort to weave classical probability theory into [a] quantum framework. (The American Mathematical Monthly)

Introduction to Stochastic Control Theory


Author: Karl J. Åström
Publisher: Courier Corporation
ISBN: 0486138275
Category: Technology & Engineering
Page: 320
View: 5799
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Exploration of stochastic control theory in terms of analysis, parametric optimization, and optimal stochastic control. Limited to linear systems with quadratic criteria; covers discrete time and continuous time systems. 1970 edition.

Introduction to Stochastic Dynamic Programming


Author: Sheldon M. Ross
Publisher: Academic Press
ISBN: 1483269094
Category: Mathematics
Page: 178
View: 2387
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Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist—providing counterexamples where appropriate—and then presents methods for obtaining such policies when they do. In addition, general areas of application are presented. The final two chapters are concerned with more specialized models. These include stochastic scheduling models and a type of process known as a multiproject bandit. The mathematical prerequisites for this text are relatively few. No prior knowledge of dynamic programming is assumed and only a moderate familiarity with probability— including the use of conditional expectation—is necessary.

An Introduction to Information Theory


Author: Fazlollah M. Reza
Publisher: Courier Corporation
ISBN: 0486158446
Category: Mathematics
Page: 528
View: 8403
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Graduate-level study for engineering students presents elements of modern probability theory, information theory, coding theory, more. Emphasis on sample space, random variables, capacity, etc. Many reference tables and extensive bibliography. 1961 edition.

Order Statistics


Author: Herbert A. David,Haikady N. Nagaraja
Publisher: John Wiley & Sons
ISBN: 0471654019
Category: Mathematics
Page: 458
View: 8664
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A completely revised and expanded edition of a classic resource In the over twenty years since the publication of the SecondEdition of Order Statistics, the theories and applications of thisdynamic field have changed markedly. Meeting the challenges anddemands of today’s students and research community, authorsH. A. David and H. N. Nagaraja return with a completely revised andupdated Order Statistics, Third Edition. Chapters two through nine of this comprehensive volume deal withfinite-sample theory, with individual topics grouped underdistribution theory (chapters two through six) and statisticalinference (chapters seven through nine). Chapters ten and elevencover asymptotic theory for central, intermediate, and extremeorder statistics, representing twice the coverage of this subjectthan the previous edition. New sections include: Stochastic orderings Characterizations Distribution-free prediction intervals Bootstrap estimations Moving order statistics Studentized range Ranked-set sampling Estimators of tail index The authors further explain application procedures for manydata-analysis techniques and quality control. An appendix providesa guide to related tables and computer algorithms. Extensiveexercise sets have been updated since the last edition. In spite ofmany eliminations, the total number of references has increasedfrom 1,000 to 1,500. Expanded coverage of shortcut methods, robust estimation, lifetesting, reliability, L-statistics, and extreme-value theorycomplete this one-of-a-kind resource. Students and researchers oforder statistics will appreciate this updated and thoroughedition.

An Introduction to Stochastic Differential Equations


Author: Lawrence C. Evans
Publisher: American Mathematical Soc.
ISBN: 1470410540
Category: Mathematics
Page: 151
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These notes provide a concise introduction to stochastic differential equations and their application to the study of financial markets and as a basis for modeling diverse physical phenomena. They are accessible to non-specialists and make a valuable addition to the collection of texts on the topic. --Srinivasa Varadhan, New York University This is a handy and very useful text for studying stochastic differential equations. There is enough mathematical detail so that the reader can benefit from this introduction with only a basic background in mathematical analysis and probability. --George Papanicolaou, Stanford University This book covers the most important elementary facts regarding stochastic differential equations; it also describes some of the applications to partial differential equations, optimal stopping, and options pricing. The book's style is intuitive rather than formal, and emphasis is made on clarity. This book will be very helpful to starting graduate students and strong undergraduates as well as to others who want to gain knowledge of stochastic differential equations. I recommend this book enthusiastically. --Alexander Lipton, Mathematical Finance Executive, Bank of America Merrill Lynch This short book provides a quick, but very readable introduction to stochastic differential equations, that is, to differential equations subject to additive ``white noise'' and related random disturbances. The exposition is concise and strongly focused upon the interplay between probabilistic intuition and mathematical rigor. Topics include a quick survey of measure theoretic probability theory, followed by an introduction to Brownian motion and the Ito stochastic calculus, and finally the theory of stochastic differential equations. The text also includes applications to partial differential equations, optimal stopping problems and options pricing. This book can be used as a text for senior undergraduates or beginning graduate students in mathematics, applied mathematics, physics, financial mathematics, etc., who want to learn the basics of stochastic differential equations. The reader is assumed to be fairly familiar with measure theoretic mathematical analysis, but is not assumed to have any particular knowledge of probability theory (which is rapidly developed in Chapter 2 of the book).

Introduction to Stochastic Processes


Author: Erhan Cinlar
Publisher: Courier Corporation
ISBN: 0486276325
Category: Mathematics
Page: 416
View: 6489
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Clear presentation employs methods that recognize computer-related aspects of theory. Topics include expectations and independence, Bernoulli processes and sums of independent random variables, Markov chains, renewal theory, more. 1975 edition.

Stochastik

Einführung in die Wahrscheinlichkeitstheorie und Statistik
Author: Hans-Otto Georgii
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110359707
Category: Mathematics
Page: 448
View: 6090
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Dieses Lehrbuch gibt eine Einführung in die "Mathematik des Zufalls", bestehend aus den beiden Teilbereichen Wahrscheinlichkeitstheorie und Statistik. Die stochastischen Konzepte, Modelle und Methoden werden durch typische Anwendungsbeispiele motiviert und anschließend systematisch entwickelt. Der dafür notwendige maßtheoretische Rahmen wird gleich zu Beginn auf elementarem Niveau bereitgestellt. Zahlreiche Übungsaufgaben, zum Teil mit Lösungsskizzen, illustrieren und ergänzen den Text. Zielgruppe sind Studierende der Mathematik ab dem dritten Semester, sowie Naturwissenschaftler und Informatiker mit Interesse an den mathematischen Grundlagen der Stochastik. Die 5. Auflage wurde nochmals bearbeitet und maßvoll ergänzt.