Sheldon Ross's classic bestseller, "Introduction to Probability Models," has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It introduces elementary probability theory and stochastic processes, and shows how probability theory can be applied fields such as engineering, computer science, management science, the physical and social sciences, and operations research.

The hallmark features of this renowned text remain in this eleventh edition: superior writing style; excellent exercises and examples covering the wide breadth of coverage of probability topic; and real-world applications in engineering, science, business and economics. The 65% new chapter material includes coverage of finite capacity queues, insurance risk models, and Markov chains, as well as updated data.
Updated data, and a list of commonly used notations and equations, instructor's solutions manualOffers new applications of probability models in biology and new material on Point Processes, including the Hawkes processIntroduces elementary probability theory and stochastic processes, and shows how probability theory can be applied in fields such as engineering, computer science, management science, the physical and social sciences, and operations researchCovers finite capacity queues, insurance risk models, and Markov chains Contains compulsory material for new Exam 3 of the Society of Actuaries including several sections in the new examsAppropriate for a full year course, this book is written under the assumption that students are familiar with calculus

The role of probability in computer science has been growing for years and, in lieu of a tailored textbook, many courses have employed a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer science graduate student (and the advanced undergraduate), best-selling author Sheldon Ross has developed the premier probability text for aspiring computer scientists involved in computer simulation and modeling. The math is precise and easily understood. As with his other texts, Sheldon Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science and related majors and practitioners.
Many interesting examples and exercises have been chosen to illuminate the techniques presented
Examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organising list problems, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queuing networks, distributed workload models, and many othersMany interesting examples and exercises have been chosen to illuminate the techniques presented

On-line and off-line quality control are the two methods used to discern a products reliability of quality. Though they are disparate techniques, both methods are used to achieve the same result. This introductory textbook integrates the two techniques to present a wide coverage of statistical methods of quality control. The text is compact, stressing the key ideas and concepts rather than trying to cover each method in complete depth. Statistical Aspects of Quality Control is an excellent starting point for a student interested in learning more about the field of statistical quality control. References and suggested readings are included at the end of each chapter.
* Presents statistical quality control in a compact fashion that stresses key ideas and concepts
* Uses the concept of Average Run Length to compare the different control charts, such as Shewhart, moving average, and cusum
* Introduces the Taguchi approach to quality design
* Includes information on acceptance sampling
* Concludes each chapter with final comments, references, and examples to illustrate the methods discussed

Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R. This book is intended for upper level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical and computer science departments. It is also appropriate for scientists, engineers and other professionals seeking a reference of foundational content and application to these fields.

Written by Sheldon Ross and Erol Peköz, this text familiarises you with advanced topics in probability while keeping the mathematical prerequisites to a minimum. Topics covered include measure theory, limit theorems, bounding probabilities and expectations, coupling and Stein's method, martingales, Markov chains, renewal theory, and Brownian motion. No other text covers all these topics rigorously but at such an accessible level - all you need is an undergraduate-level understanding of calculus and probability. New to this edition are sections on the gambler's ruin problem, Stein's method as applied to exponential approximations, and applications of the martingale stopping theorem. Extra end-of-chapter exercises have also been added, with selected solutions available.This is an ideal textbook for students taking an advanced undergraduate or graduate course in probability. It also represents a useful resource for professionals in relevant application domains, from finance to machine learning.

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Pekz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed to give familiarity with advanced topics in probability without having to wade through the exhaustive coverage of the classic advanced probability theory books. Topics include measure theory, limit theorems, bounding probabilities and expectations, coupling and Stein's method, martingales, Markov chains, renewal theory, and Brownian motion. No other text covers all these advanced topics rigorously but at such an accessible level; all you need is calculus and material from a first undergraduate course in probability.

Written for students in mathematics, computer science, operations research, statistics, and engineering, this text presents a concise lively survey of several fascinating non-calculus topics in modern applied mathematics. Sheldon Ross, noted textbook author and scientist, covers probability, mathematical finance, graphs, linear programming, statistics, computer science algorithms, and groups. He offers an abundance of interesting examples not normally found in standard finite mathematics courses: options pricing and arbitrage, tournaments, and counting formulas. The chapters assume a level of mathematical sophistication at the beginning calculus level, that is, a course in pre-calculus.

Simulation, Sixth Edition continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers will learn to apply the results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions. By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, this book presents the statistics needed to analyze simulated data and validate simulation models.

This handy supplement shows students how to come to the answers shown in the back of the text. It includes solutions to all of the odd numbered exercises.
The text itself:
In this second edition, master expositor Sheldon Ross has produced a unique work in introductory statistics. The text's main merits are the clarity of presentation, examples and applications from diverse areas, and most importantly, an explanation of intuition and ideas behind the statistical methods. To quote from the preface, "it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data." Consistent with his other excellent books in Probability and Stochastic Modeling, Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions and examples.

This textbook on the basics of option pricing is accessible to readers with limited mathematical training. It is for both professional traders and undergraduates studying the basics of finance. Assuming no prior knowledge of probability, Sheldon M. Ross offers clear, simple explanations of arbitrage, the Black-Scholes option pricing formula, and other topics such as utility functions, optimal portfolio selections, and the capital assets pricing model. Among the many new features of this third edition are new chapters on Brownian motion and geometric Brownian motion, stochastic order relations, and stochastic dynamic programming, along with expanded sets of exercises and references for all the chapters.

Written for students in mathematics, computer science, operations research, statistics, and engineering, this text presents a concise lively survey of several fascinating non-calculus topics in modern applied mathematics. Sheldon Ross, noted textbook author and scientist, covers probability, mathematical finance, graphs, linear programming, statistics, computer science algorithms, and groups. He offers an abundance of interesting examples not normally found in standard finite mathematics courses: options pricing and arbitrage, tournaments, and counting formulas. The chapters assume a level of mathematical sophistication at the beginning calculus level, that is, a course in pre-calculus.

Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods. Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples. Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered. For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises.

Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows 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. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions. A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states. Simplified Approach for Analyzing Nonhomogeneous Poisson processes Additional results on queues relating to the (a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system; (b) inspection paradox for M/M/1 queues (c) M/G/1 queue with server breakdown Many new examples and exercises.

The 5th edition of Ross s "Simulation" continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This latest edition features all-new material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables.

By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, Ross s Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.

New to this Edition: Additional material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysisAdditional material and examples on Markov chain Monte Carlo methodsUnique material on the alias method for generating discrete random variablesAdditional material on generating multivariate normal vectors"

On-line and off-line quality control are the two methods used to discern a products reliability of quality. Though they are disparate techniques, both methods are used to achieve the same result. This introductory textbook integrates the two techniques to present a wide coverage of statistical methods of quality control. The text is compact, stressing the key ideas and concepts rather than trying to cover each method in complete depth. Statistical Aspects of Quality Control is an excellent starting point for a student interested in learning more about the field of statistical quality control. References and suggested readings are included at the end of each chapter.