Introduction to Probability
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Introduction to Probability : Models and Applications

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With a focus on models and tangible applications of probability from physics, computer science, and other related disciplines, this book successfully guides readers through fundamental coverage for enhanced understanding of the problems. Topical coverage includes: bivariate discrete random, continuous random, and stochastic independence-multivariate random variables; transformations of random variables; covariance-correlation; multivariate distributions; the Central Limit Theorem; stochastic processes; and more. The book is ideal for a second course in probability and for researchers and professionals.
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Product details

  • Hardback | 624 pages
  • 182 x 264 x 35mm | 1,460g
  • New York, United States
  • English
  • 1. Auflage
  • 1118123344
  • 9781118123348
  • 1,104,268

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An essential guide to the concepts of probability theory that puts the focus on models and applications

Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors--noted experts in the field--include a review of problems where probabilistic models naturally arise, and discuss the methodology to tackle these problems.

A wide-range of topics are covered that include the concepts of probability and conditional probability, univariate discrete distributions, univariate continuous distributions, along with a detailed presentation of the most important probability distributions used in practice, with their main properties and applications.

Designed as a useful guide, the text contains theory of probability, definitions, charts, examples with solutions, illustrations, self-assessment exercises, computational exercises, problems and a glossary. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text's computer exercises Features applications representing worldwide situations and processes Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress.

Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.
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Table of contents

1 The Concept of Probability 5 1.1 Chance experiments sample spaces 6 1.2 Operations between events 16 1.3 Probability as relative frequency 35 1.4 Axiomatic definition of probability 48 1.5 Properties of probability 57 1.6 The continuity property of probability 66 1.7 Basic concepts and formulae 75 1.8 Mathematica 76 1.9 Self-assessment exercises 78 1.10 Review problems 82 1.11 Applications 89 2 Finite Sample Spaces Combinatorial Methods 97 2.1 Finite sample spaces with events of equal probability 98 2.2 Main principles of counting 109 2.3 Permutations 117 2.4 Combinations 128 2.5 The Binomial Theorem 148 2.6 Basic concepts and formulae 159 2.7 Mathematica 161 2.8 Self-assessment exercises 168 2.9 Review problems 173 2.10 Applications 182 3 Conditional Probability Independent Events 187 3.1 Conditional probability 188 3.2 The multiplicative law of probability 203 3.3 The law of total probability 213 3.4 Bayes formula 222 3.5 Independent events 229 3.6 Basic concepts and formulae 251 3.7 Mathematica exercises 252 3.8 Self-assessment exercises 256 3.9 Review problems 261 3.10 Applications 271 4 Discrete Random Variables and Distributions 277 4.1 Random variables 278 4.2 Distribution functions 285 4.3 Discrete random variables 302 4.4 Expectation of a discrete random variable 318 4.5 Variance of discrete random variables 340 4.6 Other useful results for the expectation and the variance 353 4.7 Basic concepts and formulae 364 4.8 Mathematica 365 4.9 Self-assessment exercises 372 4.10 Review problems 377 5 The Most Important Discrete Distributions 385 5.1 Bernoulli trials and the binomial distribution 386 5.2 The geometric and the negative binomial distribution 404 5.3 The hypergeometric distribution 428 5.4 The Poisson distribution 443 5.5 The Poisson process 459 5.6 Basic concepts and formulae (or Glossary and new formulae??) 471 5.7 Mathematica exercises 472 5.8 Self-assessment exercises 477 5.9 Review problems 483 5.10 Applications 494 6 Continuous Random Variables 499 6.1 Density functions 500 6.2 Distribution of a function of a random variable 519 6.3 Expectation and variance 531 6.4 Other useful results for the expectation 542 6.5 Mixture distributions 551 6.6 Basic concepts and formulae 563 6.7 Mathematica exercises 564 6.8 Self-assessment exercises 569 6.9 Review problems 575 6.10 Applications 585 7 The Most Important Continuous Distributions 589 7.1 The uniform distribution 590 7.2 The normal distribution 600 7.3 The exponential distribution 634 7.4 Other continuous distributions 646 7.5 Basic concepts and formulae 659 7.6 Mathematica exercises 662 7.7 Self-assessment exercises 666 7.8 Review problems 671
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About N. Balakrishnan

N. Balakrishnan, PhD, is a Distinguished University Professor in the Department of Mathematics and Statistics at McMaster University in Ontario, Canada. He is the author of over twenty Wiley books and served as co-editor of the Wiley's Encyclopedia of Statistical Sciences, Second Edition.

Markos V. Koutras, PhD, is Professor in the Department of Statistics and Insurance Science at the University of Piraeus, Greece.

Konstadinos G. Politis, PhD, is Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus, Greece.
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