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Probability Models and Applications

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Written by renowned experts in the field, this reissue of a textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of conditional probability that will prepare them for more advanced courses in stochastic processes.
Contents:
Introduction
The Elements of Probability Theory
Finite Probability Models and Random Sampling
Conditional Probability and Probabilistic Independence
Random Variables
Descriptive Properties of Distributions
Sums and Averages of Independent Random Variables
Special Distributions: Discrete Case
The Normal Distribution
Special Distributions: Continuous Case
Bivariate Distributions
The Bivariate Normal Distribution
Transformations of Two Random Variables
Fitting and Testing Goodness of Fit of Probability Models

Readership: Undergraduates in probability models, concepts and applications.

Pages: 734

Format: Kindle Edition

Publisher: World Scientific Publishing Company

Release: September 03, 2019

Probability Models and Applications

0/5 ( ratings)

Written by renowned experts in the field, this reissue of a textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of conditional probability that will prepare them for more advanced courses in stochastic processes.
Contents:
Introduction
The Elements of Probability Theory
Finite Probability Models and Random Sampling
Conditional Probability and Probabilistic Independence
Random Variables
Descriptive Properties of Distributions
Sums and Averages of Independent Random Variables
Special Distributions: Discrete Case
The Normal Distribution
Special Distributions: Continuous Case
Bivariate Distributions
The Bivariate Normal Distribution
Transformations of Two Random Variables
Fitting and Testing Goodness of Fit of Probability Models

Readership: Undergraduates in probability models, concepts and applications.

Pages: 734

Format: Kindle Edition

Publisher: World Scientific Publishing Company

Release: September 03, 2019

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