## A Course in Probability

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

**Publisher:**Pearson College Division

**ISBN:**9780201774719

**Category:**Mathematics

**Page:**789

**View:**6335

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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.

## A Course in Probability Theory

**Author**: Kai Lai Chung

**Publisher:**Academic Press

**ISBN:**0121741516

**Category:**Mathematics

**Page:**419

**View:**9974

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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.

## Weighing the Odds

*A Course in Probability and Statistics*

**Author**: David Williams

**Publisher:**Cambridge University Press

**ISBN:**9780521006187

**Category:**Mathematics

**Page:**547

**View:**8632

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

## A Course in Probability and Statistics

**Author**: Charles Joel Stone

**Publisher:**Cengage Learning

**ISBN:**N.A

**Category:**Mathematics

**Page:**838

**View:**1777

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This author's modern approach is intended primarily for honors undergraduates or undergraduates with a good math background taking a mathematical statistics or statistical inference course. The author takes a finite-dimensional functional modeling viewpoint (in contrast to the conventional parametric approach) to strengthen the connection between statistical theory and statistical methodology.

## A First Course in Probability

**Author**: Sheldon M. Ross

**Publisher:**Pearson College Division

**ISBN:**9780321794772

**Category:**Mathematics

**Page:**467

**View:**4698

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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.

## An Intermediate Course in Probability

**Author**: Allan Gut

**Publisher:**Springer Science & Business Media

**ISBN:**1441901620

**Category:**Mathematics

**Page:**303

**View:**7983

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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 Basic Course in Probability Theory

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

**Publisher:**Springer

**ISBN:**3319479741

**Category:**Mathematics

**Page:**265

**View:**2607

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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.

## A Course in Mathematical Statistics

**Author**: George G. Roussas

**Publisher:**Elsevier

**ISBN:**0080493149

**Category:**Mathematics

**Page:**572

**View:**4027

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A Course in Mathematical Statistics, Second Edition, contains enough material for a year-long course in probability and statistics for advanced undergraduate or first-year graduate students, or it can be used independently for a one-semester (or even one-quarter) course in probability alone. It bridges the gap between high and intermediate level texts so students without a sophisticated mathematical background can assimilate a fairly broad spectrum of the theorems and results from mathematical statistics. The coverage is extensive, and consists of probability and distribution theory, and statistical inference. * Contains 25% new material * Includes the most complete coverage of sufficiency * Transformation of Random Vectors * Sufficiency / Completeness / Exponential Families * Order Statistics * Elements of Nonparametric Density Estimation * Analysis of Variance (ANOVA) * Regression Analysis * Linear Models

## A Graduate Course in Probability

**Author**: Howard G. Tucker

**Publisher:**Courier Corporation

**ISBN:**0486493032

**Category:**Mathematics

**Page:**288

**View:**1922

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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"--

## A Second Course in Probability

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

**Publisher:**Pekozbooks

**ISBN:**9780979570407

**Category:**Mathematics

**Page:**210

**View:**6105

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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 density estimation

**Author**: Luc Devroye

**Publisher:**Birkhauser

**ISBN:**9780817633653

**Category:**Mathematics

**Page:**183

**View:**2495

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## A First Course in Probability and Markov Chains

**Author**: Giuseppe Modica,Laura Poggiolini

**Publisher:**John Wiley & Sons

**ISBN:**111847774X

**Category:**Mathematics

**Page:**352

**View:**2546

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Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores notions and structures in probability, including combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as weak and strong laws of large numbers and central limit theorem. In the second part of the book, focus is given to Discrete Time Discrete Markov Chains which is addressed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions. A First Course in Probability and Markov Chains: Presents the basic elements of probability. Explores elementary probability with combinatorics, uniform probability, the inclusion-exclusion principle, independence and convergence of random variables. Features applications of Law of Large Numbers. Introduces Bernoulli and Poisson processes as well as discrete and continuous time Markov Chains with discrete states. Includes illustrations and examples throughout, along with solutions to problems featured in this book. The authors present a unified and comprehensive overview of probability and Markov Chains aimed at educating engineers working with probability and statistics as well as advanced undergraduate students in sciences and engineering with a basic background in mathematical analysis and linear algebra.

## First Course in Probability, A: Pearson New International Edition

**Author**: Sheldon Ross

**Publisher:**Pearson Higher Ed

**ISBN:**1292037563

**Category:**Mathematics

**Page:**464

**View:**7678

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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.

## Introduction to Probability and Statistics for Engineers and Scientists

**Author**: Sheldon M. Ross

**Publisher:**Academic Press

**ISBN:**0123948428

**Category:**Mathematics

**Page:**686

**View:**6879

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Introduction to Probability and Statistics for Engineers and Scientists provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and this emphasis on data motivates the probability coverage. As with the previous editions, Ross' text has tremendously clear exposition, plus real-data examples and exercises throughout the text. Numerous exercises, examples, and applications connect probability theory to everyday statistical problems and situations. Clear exposition by a renowned expert author Real data examples that use significant real data from actual studies across life science, engineering, computing and business End of Chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material 25% New Updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science New additions to proofs in the estimation section New coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.

## A Basic Course in Measure and Probability

*Theory for Applications*

**Author**: Ross Leadbetter,Stamatis Cambanis,Vladas Pipiras

**Publisher:**Cambridge University Press

**ISBN:**1107020409

**Category:**Mathematics

**Page:**376

**View:**735

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A concise introduction covering all of the measure theory and probability most useful for statisticians.

## Probability Theory

*An Introductory Course*

**Author**: Yakov G. Sinai

**Publisher:**Springer Science & Business Media

**ISBN:**366202845X

**Category:**Mathematics

**Page:**140

**View:**445

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Sinai's book leads the student through the standard material for ProbabilityTheory, with stops along the way for interesting topics such as statistical mechanics, not usually included in a book for beginners. The first part of the book covers discrete random variables, using the same approach, basedon Kolmogorov's axioms for probability, used later for the general case. The text is divided into sixteen lectures, each covering a major topic. The introductory notions and classical results are included, of course: random variables, the central limit theorem, the law of large numbers, conditional probability, random walks, etc. Sinai's style is accessible and clear, with interesting examples to accompany new ideas. Besides statistical mechanics, other interesting, less common topics found in the book are: percolation, the concept of stability in the central limit theorem and the study of probability of large deviations. Little more than a standard undergraduate course in analysis is assumed of the reader. Notions from measure theory and Lebesgue integration are introduced in the second half of the text. The book is suitable for second or third year students in mathematics, physics or other natural sciences. It could also be usedby more advanced readers who want to learn the mathematics of probability theory and some of its applications in statistical physics.

## A First Course in Probability

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

**Publisher:**CRC Press

**ISBN:**9780849309434

**Category:**Mathematics

**Page:**467

**View:**6136

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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 Graduate Course in Probability

**Author**: Howard G. Tucker

**Publisher:**Courier Corporation

**ISBN:**0486493032

**Category:**Mathematics

**Page:**288

**View:**3694

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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"--

## A Course in Simulation

**Author**: Sheldon M. Ross

**Publisher:**MacMillan Publishing Company

**ISBN:**9780024038913

**Category:**Mathematics

**Page:**202

**View:**8325

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Mathematics of Computing -- Probability and Statistics.

## Elementary Probability Theory with Stochastic Processes

**Author**: K. L. Chung

**Publisher:**Springer Science & Business Media

**ISBN:**1475751141

**Category:**Mathematics

**Page:**325

**View:**9957

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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.