Introduction to Probability

INTRODUCTION TO PROBABILITY Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines

In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite.

This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory.

A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. 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
  Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena.

Auteur

N. Balakrishnan, PhD, is Distinguished University Professor in the Department of Mathematics and Statistics at McMaster University in Ontario, Canada. He is the author of over twenty books, including 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. He is the author/coauthor/editor of 19 books (13 in Greek, 6 in English). His research interests include multivariate analysis, combinatorial distributions, theory of runs/scans/patterns, statistical quality control, and reliability theory. Konstadinos G. Politis, PhD, is Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus. He is the author of several articles ­published in scientific journals.

Texte du rabat
Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite. This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory. A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. 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 Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena.

Contenu
1 Two-dimensional discrete random variables 1

1.1 Introduction 2

1.2 Joint probability function 2

1.3 Marginal distributions 16

1.4 Expectation of a function 25

1.5 Conditional distributions and expectations 33

1.6 Basic concepts and formulas 44

1.7 Computational exercises 45

1.8 Self-assessment exercises 50

1.8.1 True { False Questions 50

1.8.2 Multiple Choice Questions 51

1.9 Review Problems 54

1.10 Applications 59

1.10.1 Mixture distributions and reinsurance 59

2 Two-dimensional continuous random variables 65

2.1 Introduction 66

2.2 Joint density function 66

2.3 Marginal distributions 78

2.4 Expectation of a function 85

2.5 Conditional distributions and expectations 88

2.6 Geometric probability 98

2.7 Basic concepts and formulas 105

2.8 Computational exercises 106

2.9 Self-assessment exercises 113

2.9.1 True { False Questions 113

2.9.2 Multiple Choice Questions 115

2.10 Review problems 118

2.11 Applications 120

2.11.1 Modeling proportions 120

3 Independence and multivariate distributions 127

3.1 Introduction 128

3.2 Independence 128

3.3 Properties of independent random variables 144

3.4 Multivariate joint distributions 150

3.5 Independence of more than two variables 164

3.6 Distribution of an ordered sample 175

3.7 Basic concepts and formulas 187

3.8 Computational exercises 191

3.9 Self-assessment exercises 198

3.9.1 True { False Questions 198

3.9.2 Multiple Choice Questions 200

3.10 Review Problems 204

3.11 Applications 210

3.11.1 Acceptance sampling 210

4 Transformations of variables 217

4.1 Introduction 218

4.2 Joint distribution for functions of variables 218

4.3 Distributions of sum, di
erence, product, quotient 227

4.4 ­2, t and F distributions 240

4.5 Basic concepts and formulas 256

4.6 Computational exercises 258

4.7 Self-assessment exercises 263

4.7.1 True { False Questions 263

4.7.2 Multiple Choice Questions 265

4.8 Review problems 268

4.9 Applications 4 272

4.9.1 Random number generators coverage { Planning under

random event occurrences 272

5 Covariance and correlation 279

5.1 Introduction 279

5.2 Covariance 280

5.3 Correlation coe
cient 295

5.4 Conditional expectation and variance 305

5.5 Regression curves 318

5.6 Basic concepts and formulas 333

5.7 Computational exercises 335

5.8 Self-assessment exercises 342

5.8.1 True { False Questions 342

5.8.2 Multiple Choice Questions 344

5.9 Review problems 348

5.10 Applications 355

5.10.1 Portfolio Optimization Theory (Markowitz (1952)) 355

6 Important multivaria…