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1. A first course in probability [2019]
 Ross, Sheldon M., author.
 Tenth edition.  Boston : Pearson, 2018.
 Description
 Book — xii, 505 pages : illiustrations ; 26 cm
 Summary

 Combinatorial analysis
 Axioms of probability
 Conditional probability and independence
 Random variables
 Continuous random variables
 Jointly distributed random variables
 Properties of expectation
 Limit theorems
 Additional topics in probability
 Simulation
 Common discrete distributions
 Common continuous distributions.
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2. Introductory statistics [2017]
 Ross, Sheldon M., author.
 Fourth edition.  London ; San Diego, CA : Academic Press is an imprint of Elsevier, [2017]
 Description
 Book — xxvii, 796 pages ; 25 cm
 Summary

 1. Preface
 2. Introduction to Statistics
 3. Describing Data Sets Using Statistics to Summarize
 4. Data Sets Probability
 5. Discrete Random Variables
 6. Normal Random Variables
 7. Distributions of Sampling
 8. Statistics Estimation Testing
 9. Statistical Hypotheses
 10. Hypothesis Tests Concerning Two Populations
 11. Analysis of Variance Linear Regression
 12. ChiSquared Goodness of Fit Tests
 13. Nonparametric Hypotheses
 14. Tests
 15. Quality Control
 16. Appendices.
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3. A first course in probability [2014]
 Ross, Sheldon M.
 Ninth edition.  Upper Saddle River, New Jersey : Pearson Education, Inc., [2014]
 Description
 Book — xi, 467 pages ; 26 cm.
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4. Introduction to probability models [2014]
 Ross, Sheldon M., author.
 Eleventh edition.  Oxford : Academic Press is an imprint of Elsevier, 2014.
 Description
 Book — xv, 767 pages ; 24 cm
 Summary

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 realworld 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 manual* Offers new applications of probability models in biology and new material on Point Processes, including the Hawkes process* Introduces 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 research* Covers 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 exams* Appropriate for a full year course, this book is written under the assumption that students are familiar with calculus.
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5. A first course in probability [2010]
 Ross, Sheldon M.
 8th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2010.
 Description
 Book — xiii, 530 p. : ill. ; 26 cm.
 Summary

 1. Combinatorial Analysis
 2. Axioms of Probability
 3. Conditional Probability and Independence
 4. Random Variables
 5. Continuous Random Variables
 6. Jointly Distributed Random Variables
 7. Properties of Expectation
 8. Limit Theorems
 9. Additional Topics in Probability
 10. Simulation Appendix A. Answers to Selected Problems Appendix B. Solutions to SelfTest Problems and Exercises Index.
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6. Introduction to probability models [2010]
 Ross, Sheldon M.
 10th ed.  Amsterdam ; Boston : Elsevier/Academic Press, c2010.
 Description
 Book — xv, 784 p. : ill. ; 24 cm.
 Summary

Ross' 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. New to this Edition are 65 per cent new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains. It contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams. It has updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, test bank, and companion website. it 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; and realworld applications in engineering, science, business and economics.
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 Ross, Sheldon M.
 4th ed.  Amsterdam ; Boston : Academic Press, c2009.
 Description
 Book — xv, 664 p. : ill. (some col.) ; 25 cm. + 1 CDROM (4 3/4 in.)
 Summary

This updated text provides a superior introduction to applied probability and statistics for engineering or science majors. It 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, this text has tremendously clear exposition, plus realdata examples and exercises throughout the text. Numerous exercises, examples, and applications apply probability theory to everyday statistical problems and situations. New to the 4th Edition: new chapter on Simulation, Bootstrap Statistical Methods, and Permutation Tests; 20% new updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science; new real data examples that use significant real data from actual studies across life science, engineering, computing and business; and, new end of chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material.
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8. Introduction to probability models [2007]
 Ross, Sheldon M.
 9th ed.  Amsterdam ; Boston : Elsevier/Academic Press, c2007.
 Description
 Book — xviii, 782 p. : ill. ; 24 cm.
 Summary

 Preface
 1. Introduction to Probability Theory
 2. Random Variables
 3. Conditional Probability and Conditional Expectation
 4. Markov Chains
 5. The Exponential Distribution and the Poisson Process
 6. ContinuousTime Markov Chains
 7. Renewal Theory and Its Applications
 8. Queueing Theory
 9. Reliability Theory
 10. Brownian Motion and Stationary Processes
 11. Simulation Appendix: Solutions to Starred Exercises Index.
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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.
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9. A second course in probability [2007]
 Ross, Sheldon M.
 Boston : ProbabilityBookstore.com, 2007.
 Description
 Book — 210 pages : illustrations ; 25 cm
 Summary

 1. Measure Theory and Laws of Large Numbers
 Introduction
 A NonMeasurable Event
 Countable and Uncountable Sets
 Probability Spaces
 Random Variables
 Expected Value
 Almost Sure Convergence and the Dominated Convergence Theorem
 Convergence in Probablitiy and in Distribution
 Law of Large Numbers and Ergodic Theorem
 Exercises
 2. Stein's Method and Central Limit Theorems
 Introduction
 Coupling
 Poisson Approximation and Le Cam's Theorem
 The SteinChen Method
 Stein's Method for the Geometric Distribution
 Stein's Method for the Normal Distribution
 Exercises
 3. Conditional Expectation and Martingales
 Introduction
 Conditional Expectation
 Martingales
 The Martingale Stopping Theorem
 The HoeffdingAzuma Inequality
 Submartingales, Supermartingales, and a Convergence Theorem
 Exercises
 4. Bounding Probabilities and Expectations
 Introduction
 Jensen's Inequality
 Probability Bounds via the Importance Sampling Identity
 Chernoff Bounds
 Second Moment and Conditional Expectation Inequalities
 The MinMax Identity and Bounds on the Maximum
 Stochastic Orderings
 Exercises
 5. Markov Chains
 Introduction
 The Transition Matrix
 The Strong Markov Property
 Classification of States
 Stationary and Limiting Distributions
 Time Reversibility
 A Mean Passage Time Bound
 Exercises
 6. Renewal Theory
 Introduction
 Some Limit Theorems of Renewal Theory
 Renewal Reward Processes
 6.3.1 Queueing Theory Applications of Renewal Reward Processes
 Blackwell's Theorem
 The Poisson Process
 Exercises
 7. Brownian Motion
 Introduction
 Continuous Time Martingales
 Construction Brownian Motion
 Embedding Variables in Brownian Motion
 The Central Limit Theorem
 Exercises.
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10. A first course in probability [2006]
 Ross, Sheldon M.
 7th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2006.
 Description
 Book — x, 565 p. : ill. ; 25 cm.
 Summary

 1. Combinatorial Analysis.
 2. Axioms of Probability.
 3. Conditional Probability and Independence.
 4. Random Variables.
 5. Continuous Random Variables.
 6. Jointly Distributed Random Variables.
 7. Properties of Expectation.
 8. Limit Theorems.
 9. Additional Topics in Probability.
 10. Simulation. Appendix A. Answers to Selected Problems. Appendix B. Solutions to SelfTest Problems and Exercises. Index.
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11. Simulation [2006]
 Ross, Sheldon M.
 4th ed.  Amsterdam ; Boston : Elsevier Academic Press, c2006.
 Description
 Book — xiii, 298 p. : ill. ; 24 cm.
 Summary

 Preface Introduction Elements of Probability Random Numbers Generating Discrete Random Variables Generating Continuous Random Variables The Discrete Event Simulation Approach Statistical Analysis of Simulated Data Variance Reduction Techniques Statistical Validation Techniques Markov Chain Monte Carlo Methods Some Additional Topics Exercises References Index.
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12. A first course in probability [2002]
 Ross, Sheldon M.
 6th ed.  Upper Saddle River, N.J. : Prentice Hall, c2002.
 Description
 Book — viii, 520 p. : ill. ; 25 cm.
 Summary

 (NOTE: Each chapter concludes with Summary, Problems, Theoretical Exercises, and SelfTest Problems and Exercises.)
 1. Combinatorial Analysis. Introduction. The Basic Principle of Counting. Permutations. Combinations. Multinomial Coefficients. The Number of Integer Solutions of Equations.
 2. Axioms of Probability. Introduction. Sample Space and Events. Axioms of Probability. Some Simple Propositions. Sample Spaces Having Equally Likely Outcomes. Probability As a Continuous Set Function. Probability As a Measure of Belief.
 3. Conditional Probability and Independence. Introduction. Conditional Probabilities. Bayes' Formula. Independent Events. P(*
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13. Simulation [2002]
 Ross, Sheldon M.
 3rd ed.  San Diego : Academic Press, c2002.
 Description
 Book — xiii, 274 p. : ill. ; 24 cm.
 Summary

 Preface Introduction Elements of Probability Random Numbers Generating Discrete Random Variables Generating Continuous Random Variables The Discrete Event Simulation Approach Statistical Analysis of Simulated Data Variance Reduction Techniques Statistical Validation Techniques Markov Chain Monte Carlo Methods Some Additional Topics Exercises References Index.
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14. A first course in probability [1998]
 Ross, Sheldon M.
 5th ed.  Upper Saddle River, N.J. : Prentice Hall, c1998.
 Description
 Book — xiv, 514 p. : ill. ; 25 cm. + 1 computer disk (3 1/2 in.)
 Summary

 *Combinatorial Analysis *Axioms of Probability *Conditional Probability and Independence *Random Variables *Continuous Random Variables *Jointly Distributed Random Variables *Properties of Expectation *Limit Theorems *Additional Topics in Probability *Simulation *Appendix A. Answers to Selected Problems *Appendix B. Solutions to SelfTest Problems and Exercises *Index.
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15. Simulation [1997]
 Ross, Sheldon M.
 2nd ed.  San Diego : Academic Press, c1997.
 Description
 Book — xii, 282 p. : ill. ; 24 cm.
 Summary

 Part 1 Elements of probability: sample space and events axioms of probability conditional probability and independence random variables expectation variance Chebyshev's inequality and the laws of large numbers some discrete random variables continuous random variables conditional expectation and conditional variance problems.
 Part 2 Random numbers: pseudorandom number generation using random numbers to evaluate integrals.
 Part 3 Generating discrete random variables: the inverse transform method generating a Poisson random variable generating binomial random variables the acceptancerejection technique the composition approach.
 Part 4 Generating continuous random variables: the inverse transform algorithm the rejection method the polar method for generating normal random variables generating a Poisson process generating a nonhomogeneous Poisson process.
 Part 5 The discrete event simulation approach: simulation via discrete events a single server queueing system a queueing system with two servers in series a queueing system with two parallel servers an inventory model a repair problem exercising a stock option verification of the simulation model problems.
 Part 6 Statistical analysis of simulated data: the sample means and sample variance interval estimates of a population mean the bootstrapping technique for estimating mean square errors.
 Part 7 Variance reduction techniques: the use of antipathetic variables the use of control variates variance reduction by conditioning stratified sampling importance sampling using common random numbers.
 Part 8 Statistical validation techniques: goodness of fit tests goodness of fit tests when some parameters are unspecified the twosample problem validating the assumption of a nonhomogeneous Poisson process.
 Part 9 Markov chain Monte Carlo methods: Markov chains the HastingsMetropolis algorithm the Gibbs sampler simulated annealing the sampling importance resampling algorithm.
 Part 10 Some additional topics: the alias method for generating discrete random variables simulating a twodimensional Poisson process simulation applications of an identity for sums of Bernoulli random variables estimating probabilities and expected first passage times by using random hazards appendix.
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16. Stochastic processes [1996]
 Ross, Sheldon M.
 2nd ed.  New York : Wiley, c1996.
 Description
 Book — xv, 510 p. : ill. ; 25 cm.
 Summary

 Preliminaries. The Poisson Process. Renewal Theory. Markov Chains. ContinuousTime Markov Chains. Martingales. Random Walks. Brownian Motion and Other Markov Processes. Stochastic Order Relations. Poisson Approximations. Answers and Solutions to Selected Problems. Index.
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QA274 .R65 1996  Unknown 
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17. A first course in probability [1994]
 Ross, Sheldon M.
 4th ed.  New York : Macmillan College Pub. Co. ; Toronto : Maxwell Macmilllan Canada ; New York : Maxwell Macmillan International, c1994.
 Description
 Book — xv, 473 p. : ill. ; 25 cm.
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18. A first course in probability [1988]
 Ross, Sheldon M.
 3rd ed.  New York : Macmillan ; London : Collier Macmillan, c1988.
 Description
 Book — x, 420 p. : ill. ; 25 cm.
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QA273 .R83 1988  Unknown 
 Ross, Sheldon M.
 New York, N.Y. : Wiley, c1987.
 Description
 Book — xv, 492 p. : ill. ; 25 cm.
 Summary

 Elements of Probability. Random Variables and Expectation. Special Random Variables. Sampling. Parameter Estimation. Hypothesis Testing. Regression. Analysis of Variance. Goodness of Fit and Nonparametric Testing. Life Testing. Quality Control. Simulation. Appendix of Programs. Appendix of Tables. Index.
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TA340 .R67 1987  Available 
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20. A first course in probability [1984]
 Ross, Sheldon M.
 2nd ed.  New York : Macmillan ; London : Collier Macmillan, c1984.
 Description
 Book — xii, 392 p. : ill. ; 25 cm.
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QA273 .R83 1984  Unknown 