**Author**: Willliam Feller

**Publisher:**John Wiley & Sons

**ISBN:**9788126518067

**Category:**

**Page:**700

**View:**7152

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# Search Results for: an-introduction-to-probability-theory-and-its-applications-2nd-ed

**Author**: Willliam Feller

**Publisher:** John Wiley & Sons

**ISBN:** 9788126518067

**Category:**

**Page:** 700

**View:** 7152

· The Exponential and the Uniform Densities· Special Densities. Randomization· Densities in Higher Dimensions. Normal Densities and Processes· Probability Measures and Spaces· Probability Distributions in Rr· A Survey of Some Important Distributions and Processes· Laws of Large Numbers. Applications in Analysis· The Basic Limit Theorems· Infinitely Divisible Distributions and Semi-Groups· Markov Processes and Semi-Groups· Renewal Theory· Random Walks in R1· Laplace Transforms. Tauberian Theorems. Resolvents· Applications of Laplace Transforms· Characteristic Functions· Expansions Related to the Central Limit Theorem,· Infinitely Divisible Distributions· Applications of Fourier Methods to Random Walks· Harmonic Analysis
*Volume I: Elementary Theory and Methods*

**Author**: D.J. Daley,D. Vere-Jones

**Publisher:** Springer Science & Business Media

**ISBN:** 0387955410

**Category:** Mathematics

**Page:** 471

**View:** 3300

Point processes and random measures find wide applicability in telecommunications, earthquakes, image analysis, spatial point patterns, and stereology, to name but a few areas. The authors have made a major reshaping of their work in their first edition of 1988 and now present their Introduction to the Theory of Point Processes in two volumes with sub-titles Elementary Theory and Models and General Theory and Structure. Volume One contains the introductory chapters from the first edition, together with an informal treatment of some of the later material intended to make it more accessible to readers primarily interested in models and applications. The main new material in this volume relates to marked point processes and to processes evolving in time, where the conditional intensity methodology provides a basis for model building, inference, and prediction. There are abundant examples whose purpose is both didactic and to illustrate further applications of the ideas and models that are the main substance of the text.

**Author**: Ming Li,Paul Vitanyi

**Publisher:** Springer Science & Business Media

**ISBN:** 1475738609

**Category:** Computers

**Page:** 550

**View:** 602

**Author**: Charles Miller Grinstead,James Laurie Snell

**Publisher:** American Mathematical Soc.

**ISBN:** 0821894145

**Category:** Probabilities

**Page:** 510

**View:** 3618

This text is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science. It presents a thorough treatment of ideas and techniques necessary for a firm understanding of the subject. The text is also recommended for use in discrete probability courses. The material is organized so that the discrete and continuous probability discussions are presented in a separate, but parallel, manner. This organization does not emphasize an overly rigorous or formal view of probability and therefore offers some strong pedagogical value. Hence, the discrete discussions can sometimes serve to motivate the more abstract continuous probability discussions. Features: Key ideas are developed in a somewhat leisurely style, providing a variety of interesting applications to probability and showing some nonintuitive ideas. Over 600 exercises provide the opportunity for practicing skills and developing a sound understanding of ideas. Numerous historical comments deal with the development of discrete probability. The text includes many computer programs that illustrate the algorithms or the methods of computation for important problems. The book is a beautiful introduction to probability theory at the beginning level. The book contains a lot of examples and an easy development of theory without any sacrifice of rigor, keeping the abstraction to a minimal level. It is indeed a valuable addition to the study of probability theory. --Zentralblatt MATH

**Author**: K. L. Chung

**Publisher:** Springer Science & Business Media

**ISBN:** 1475751141

**Category:** Mathematics

**Page:** 325

**View:** 3219

In the past half-century the theory of probability has grown from a minor isolated theme into a broad and intensive discipline interacting with many other branches of mathematics. At the same time it is playing a central role in the mathematization of various applied sciences such as statistics, opera tions research, biology, economics and psychology-to name a few to which the prefix "mathematical" has so far been firmly attached. The coming-of-age of probability has been reflected in the change of contents of textbooks on the subject. In the old days most of these books showed a visible split personality torn between the combinatorial games of chance and the so-called "theory of errors" centering in the normal distribution. This period ended with the appearance of Feller's classic treatise (see [Feller l]t) in 1950, from the manuscript of which I gave my first substantial course in probability. With the passage of time probability theory and its applications have won a place in the college curriculum as a mathematical discipline essential to many fields of study. The elements of the theory are now given at different levels, sometimes even before calculus. The present textbook is intended for a course at about the sophomore level. It presupposes no prior acquaintance with the subject and the first three chapters can be read largely without the benefit of calculus.

**Author**: Olav Kallenberg

**Publisher:** Springer Science & Business Media

**ISBN:** 0387227040

**Category:** Mathematics

**Page:** 523

**View:** 5903

Unique for its broad and yet comprehensive coverage of modern probability theory, ranging from first principles and standard textbook material to more advanced topics. In spite of the economical exposition, careful proofs are provided for all main results. After a detailed discussion of classical limit theorems, martingales, Markov chains, random walks, and stationary processes, the author moves on to a modern treatment of Brownian motion, L=82vy processes, weak convergence, It=93 calculus, Feller processes, and SDEs. The more advanced parts include material on local time, excursions, and additive functionals, diffusion processes, PDEs and potential theory, predictable processes, and general semimartingales. Though primarily intended as a general reference for researchers and graduate students in probability theory and related areas of analysis, the book is also suitable as a text for graduate and seminar courses on all levels, from elementary to advanced. Numerous easy to more challenging exercises are provided, especially for the early chapters. From the author of "Random Measures".

**Author**: Henk Tijms

**Publisher:** Cambridge University Press

**ISBN:** 1139511076

**Category:** Mathematics

**Page:** N.A

**View:** 5345

Understanding Probability is a unique and stimulating approach to a first course in probability. The first part of the book demystifies probability and uses many wonderful probability applications from everyday life to help the reader develop a feel for probabilities. The second part, covering a wide range of topics, teaches clearly and simply the basics of probability. This fully revised third edition has been packed with even more exercises and examples and it includes new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion, and a new chapter on continuous-time Markov chains with applications. Here you will find all the material taught in an introductory probability course. The first part of the book, with its easy-going style, can be read by anybody with a reasonable background in high school mathematics. The second part of the book requires a basic course in calculus.

**Author**: Vijay K. Rohatgi,A.K. Md. Ehsanes Saleh

**Publisher:** John Wiley & Sons

**ISBN:** 1118799658

**Category:** Mathematics

**Page:** 728

**View:** 1497

A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.
*Second Edition*

**Author**: Kian Guan Lim

**Publisher:** World Scientific Publishing Company

**ISBN:** 9814641952

**Category:** Business & Economics

**Page:** 536

**View:** 4241

This book is an introduction to the mathematical analysis of probability theory and provides some understanding of how probability is used to model random phenomena of uncertainty, specifically in the context of finance theory and applications. The integrated coverage of both basic probability theory and finance theory makes this book useful reading for advanced undergraduate students or for first-year postgraduate students in a quantitative finance course. The book provides easy and quick access to the field of theoretical finance by linking the study of applied probability and its applications to finance theory all in one place. The coverage is carefully selected to include most of the key ideas in finance in the last 50 years. The book will also serve as a handy guide for applied mathematicians and probabilists to easily access the important topics in finance theory and economics. In addition, it will also be a handy book for financial economists to learn some of the more mathematical and rigorous techniques so their understanding of theory is more rigorous. It is a must read for advanced undergraduate and graduate students who wish to work in the quantitative finance area.

**Author**: Richard Scheaffer,Linda Young

**Publisher:** Cengage Learning

**ISBN:** 0534386717

**Category:** Mathematics

**Page:** 480

**View:** 1821

This text focuses on the utility of probability in solving real-world problems for students in a one-semester calculus-based probability course. Theory is developed to a practical degree and grounded in discussion of its practical uses in solving real-world problems. Numerous applications using up-to-date real data in engineering and the life, social, and physical sciences illustrate and motivate the many ways probability affects our lives. The text’s accessible presentation carefully progresses from routine to more difficult problems to suit students of different backgrounds, and carefully explains how and where to apply methods. Students going on to more advanced courses in probability and statistics will gain a solid background in fundamental concepts and theory, while students who must apply probability to their courses engineering and the sciences will develop a working knowledge of the subject and appreciation of its practical power. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Author**: V. K. Rohatgi

**Publisher:** John Wiley & Sons Inc

**ISBN:** N.A

**Category:** Mathematics

**Page:** 684

**View:** 1537

Probability; Random variables and their probability distributions; Moments and generating functions; Random vectors; Some special distributions; Limit theorems; Sample moments and their distributions; The theory of point estimation; Neyman-Pearson theory of testing of hypotheses; Some further results on hypotheses testing; Confidence estimation; The general linear hypothesis; Nonparametric statistical inference; Sequential statistical inference.
*Third Edition*

**Author**: Jordan M. Stoyanov

**Publisher:** Courier Corporation

**ISBN:** 0486499987

**Category:** Education

**Page:** 368

**View:** 3874

"While most mathematical examples illustrate the truth of a statement, counterexamples demonstrate a statement's falsity. Enjoyable topics of study, counterexamples are valuable tools for teaching and learning. The definitive book on the subject in regards to probability, this third edition features the author's revisions and corrections plus a substantial new appendix. 2013 edition"--
*Second Edition*

**Author**: Jeffrey S Rosenthal

**Publisher:** World Scientific Publishing Company

**ISBN:** 9813101652

**Category:** Mathematics

**Page:** 236

**View:** 3163

Solutions Manual for Free Download This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results. Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. In this new edition, many exercises and small additional topics have been added and existing ones expanded. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.

**Author**: Dimitri P. Bertsekas,John N. Tsitsiklis

**Publisher:** N.A

**ISBN:** 9781886529236

**Category:** Mathematics

**Page:** 528

**View:** 1101

**Author**: Rabi Bhattacharya,Edward C. Waymire

**Publisher:** Springer

**ISBN:** 3319479741

**Category:** Mathematics

**Page:** 265

**View:** 6212

This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. In this second edition, the text has been reorganized for didactic purposes, new exercises have been added and basic theory has been expanded. General Markov dependent sequences and their convergence to equilibrium is the subject of an entirely new chapter. The introduction of conditional expectation and conditional probability very early in the text maintains the pedagogic innovation of the first edition; conditional expectation is illustrated in detail in the context of an expanded treatment of martingales, the Markov property, and the strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two topics to highlight. A selection of large deviation and/or concentration inequalities ranging from those of Chebyshev, Cramer–Chernoff, Bahadur–Rao, to Hoeffding have been added, with illustrative comparisons of their use in practice. This also includes a treatment of the Berry–Esseen error estimate in the central limit theorem. The authors assume mathematical maturity at a graduate level; otherwise the book is suitable for students with varying levels of background in analysis and measure theory. For the reader who needs refreshers, theorems from analysis and measure theory used in the main text are provided in comprehensive appendices, along with their proofs, for ease of reference. Rabi Bhattacharya is Professor of Mathematics at the University of Arizona. Edward Waymire is Professor of Mathematics at Oregon State University. Both authors have co-authored numerous books, including a series of four upcoming graduate textbooks in stochastic processes with applications.
*with Applications in R*

**Author**: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

**Publisher:** Springer Science & Business Media

**ISBN:** 1461471389

**Category:** Mathematics

**Page:** 426

**View:** 4829

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

**Author**: Lee J. Bain,Max Engelhardt

**Publisher:** Duxbury Press

**ISBN:** 9780534380205

**Category:** Mathematics

**Page:** 644

**View:** 9815

The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models. Lee J. Bain and Max Engelhardt focus on the mathematical development of the subject, with examples and exercises oriented toward applications.

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