## A Course in Probability

**Author**: Neil A. Weiss,Paul T. Holmes,Michael Hardy

**Publisher:**Pearson College Division

**ISBN:**9780201774719

**Category:**Mathematics

**Page:**789

**View:**4505

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This text is intended primarily for readers interested in mathematical probability as applied to mathematics, statistics, operations research, engineering, and computer science. It is also appropriate for mathematically oriented readers in the physical and social sciences. Prerequisite material consists of basic set theory and a firm foundation in elementary calculus, including infinite series, partial differentiation, and multiple integration. Some exposure to rudimentary linear algebra (e.g., matrices and determinants) is also desirable. This text includes pedagogical techniques not often found in books at this level, in order to make the learning process smooth, efficient, and enjoyable. Fundamentals of Probability: Probability Basics. Mathematical Probability. Combinatorial Probability. Conditional Probability and Independence.Discrete Random Variables: Discrete Random Variables and Their Distributions. Jointly Discrete Random Variables. Expected Value of Discrete Random Variables.Continuous Random Variables: Continuous Random Variables and Their Distributions. Jointly Continuous Random Variables. Expected Value of Continuous Random Variables.Limit Theorems and Advanced Topics: Generating Functions and Limit Theorems. Additional Topics. For all readers interested in probability.

## Weighing the Odds

*A Course in Probability and Statistics*

**Author**: David Williams

**Publisher:**Cambridge University Press

**ISBN:**9780521006187

**Category:**Mathematics

**Page:**547

**View:**3458

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An advanced textbook; with many examples and exercises, often with hints or solutions; code is provided for computational examples and simulations.

## A Course in Probability Theory

**Author**: Kai Lai Chung

**Publisher:**Academic Press

**ISBN:**0121741516

**Category:**Mathematics

**Page:**419

**View:**2158

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Since the publication of the first edition of this classic textbook over thirty years ago, tens of thousands of students have used A Course in Probability Theory. New in this edition is an introduction to measure theory that expands the market, as this treatment is more consistent with current courses. While there are several books on probability, Chung's book is considered a classic, original work in probability theory due to its elite level of sophistication.

## Outlines and Highlights for a Course in Probability by Weiss, Isbn

*9780201774719*

**Author**: Cram101 Textbook Reviews

**Publisher:**Academic Internet Pub Incorporated

**ISBN:**9781617442148

**Category:**Education

**Page:**228

**View:**7724

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Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780201774719 .

## Введение в теорию вероятностей и ее приложения

**Author**: В. Феллер

**Publisher:**Рипол Классик

**ISBN:**5458261208

**Category:**Science

**Page:**772

**View:**7644

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Большое число примеров применений теории в физике, биологии и экономике. Вместе с первым томом он составляет прекрасное учебное руководство, в котором очень удачно сочетаются и принципиальные основы, и важнейшие приложения теории вероятностей.

## A Basic Course in Probability Theory

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

**Publisher:**Springer Science & Business Media

**ISBN:**0387719393

**Category:**Mathematics

**Page:**220

**View:**7845

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Introductory Probability is a pleasure to read and provides a fine answer to the question: How do you construct Brownian motion from scratch, given that you are a competent analyst? There are at least two ways to develop probability theory. The more familiar path is to treat it as its own discipline, and work from intuitive examples such as coin flips and conundrums such as the Monty Hall problem. An alternative is to first develop measure theory and analysis, and then add interpretation. Bhattacharya and Waymire take the second path.

## A Graduate Course in Probability

**Author**: Howard G. Tucker

**Publisher:**Courier Corporation

**ISBN:**0486493032

**Category:**Mathematics

**Page:**288

**View:**5033

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"Suitable for a graduate course in analytic probability, this text requires only a limited background in real analysis. Topics include probability spaces and distributions, stochastic independence, basic limiting options, strong limit theorems for independent random variables, central limit theorem, conditional expectation and Martingale theory, and an introduction to stochastic processes"--

## An Intermediate Course in Probability

**Author**: Allan Gut

**Publisher:**Springer Science & Business Media

**ISBN:**1441901620

**Category:**Mathematics

**Page:**303

**View:**8020

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This is the only book that gives a rigorous and comprehensive treatment with lots of examples, exercises, remarks on this particular level between the standard first undergraduate course and the first graduate course based on measure theory. There is no competitor to this book. The book can be used in classrooms as well as for self-study.

## A First Course in Probability

**Author**: Tapas K. Chandra,Dipak Chatterjee

**Publisher:**CRC Press

**ISBN:**9780849309434

**Category:**Mathematics

**Page:**467

**View:**4287

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The advancement of science in the twentieth century is marked by a special feature -- its transition from deterministic phenomena to probabilistic phenomena. For this reason the probability theory is introduced at the earliest possible level of any academic pursuit. Written at an introductory level, A First Course in Probability takes an intuitive approach to proving the ethereal existence of probability, developing the subject step-by-step to show the accessibility of probability theory. The authors provide hundreds of problems from almost all spheres of life to demonstrate how probability plays a decisive role. Numerous routine and simple examples are solved throughout the text to demonstrate various techniques of solving practical problems. The more difficult problems are solved at the end of each chapter under the heading, "Miscellaneous Examples," and these are useful in solving problems in different competitive examinations. Easy to understand and up-to-date, the text incorporates all the fundamental results while bringing forth the latest results. Some topics that can be avoided in the first reading are star-marked in the text.

## A Course in Real Analysis

**Author**: John N. McDonald,Neil A. Weiss

**Publisher:**N.A

**ISBN:**9780123877741

**Category:**Mathematics

**Page:**667

**View:**9196

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A Course in Real Analysis provides a firm foundation in real analysis concepts and principles while presenting a broad range of topics in a clear and concise manner. This student-oriented text balances theory and applications, and contains a wealth of examples and exercises. Throughout the text, the authors adhere to the idea that most students learn more efficiently by progressing from the concrete to the abstract. McDonald and Weiss have also created real application chapters on probability theory, harmonic analysis, and dynamical systems theory. The text offers considerable flexibility in the choice of material to cover. * Motivation of Key Concepts: The importance of and rationale behind key ideas are made transparent * Illustrative Examples: Roughly 200 examples are presented to illustrate definitions and results * Abundant and Varied Exercises: Over 1200 exercises are provided to promote understanding * Biographies: Each chapter begins with a brief biography of a famous mathematician

## A First Course in Probability

**Author**: Sheldon M. Ross

**Publisher:**Pearson College Division

**ISBN:**9780321794772

**Category:**Mathematics

**Page:**467

**View:**8647

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A First Course in Probability, Ninth Edition, features clear and intuitive explanations of the mathematics of probability theory, outstanding problem sets, and a variety of diverse examples and applications. This book is ideal for an upper-level undergraduate or graduate level introduction to probability for math, science, engineering and business students. It assumes a background in elementary calculus.

## Probability Theory

*A First Course in Probability Theory and Statistics*

**Author**: Werner Linde

**Publisher:**Walter de Gruyter GmbH & Co KG

**ISBN:**3110466198

**Category:**Mathematics

**Page:**409

**View:**5723

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This book is intended as an introduction to Probability Theory and Mathematical Statistics for students in mathematics, the physical sciences, engineering, and related fields. It is based on the author’s 25 years of experience teaching probability and is squarely aimed at helping students overcome common difficulties in learning the subject. The focus of the book is an explanation of the theory, mainly by the use of many examples. Whenever possible, proofs of stated results are provided. All sections conclude with a short list of problems. The book also includes several optional sections on more advanced topics. This textbook would be ideal for use in a first course in Probability Theory. Contents: Probabilities Conditional Probabilities and Independence Random Variables and Their Distribution Operations on Random Variables Expected Value, Variance, and Covariance Normally Distributed Random Vectors Limit Theorems Mathematical Statistics Appendix Bibliography Index

## A First Course in Probability and Statistics

**Author**: B. L. S. Prakasa Rao

**Publisher:**World Scientific

**ISBN:**9812836535

**Category:**Mathematics

**Page:**317

**View:**7378

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This book provides a clear exposition of the theory of probability along with applications in statistics.

## A Course in Simulation

**Author**: Sheldon M. Ross

**Publisher:**MacMillan Publishing Company

**ISBN:**9780024038913

**Category:**Mathematics

**Page:**202

**View:**3068

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Mathematics of Computing -- Probability and Statistics.

## A Course in Mathematical Statistics and Large Sample Theory

**Author**: Rabi Bhattacharya,Lizhen Lin,Victor Patrangenaru

**Publisher:**Springer

**ISBN:**1493940325

**Category:**Mathematics

**Page:**389

**View:**1578

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This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.

## Introduction to Probability Models

**Author**: Sheldon M. Ross

**Publisher:**Academic Press

**ISBN:**9780123756879

**Category:**Mathematics

**Page:**800

**View:**4613

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Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students. This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes. New to this Edition: 65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains Contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams Updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, and test bank Includes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field Hallmark features: Superior writing style Excellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics

## A Course in Time Series Analysis

**Author**: Daniel Peña,George C. Tiao,Ruey S. Tsay

**Publisher:**John Wiley & Sons

**ISBN:**1118031229

**Category:**Mathematics

**Page:**496

**View:**3252

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New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

## A Second Course in Probability

**Author**: Sheldon M. Ross,Erol A. Peköz

**Publisher:**Pekozbooks

**ISBN:**9780979570407

**Category:**Mathematics

**Page:**210

**View:**1093

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Written for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science, this guided tour discusses advanced topics in probability including measure theory, limit theorems, bounding probabilities and expectations, coupling and Steins method, martingales, Markov chains, renewal theory, and Brownian motion. (Mathematics)

## A Course in Statistics with R

**Author**: Prabhanjan N. Tattar,Suresh Ramaiah,B. G. Manjunath

**Publisher:**John Wiley & Sons

**ISBN:**1119152739

**Category:**Computers

**Page:**696

**View:**8224

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Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets