Probability for Engineering, Mathematics, and Science

ISBN-10: 1111430276
ISBN-13: 9781111430276
Edition: 2012
Authors: Chris P. Tsokos
List price: $269.95 Buy it from $4.24 Rent it from $109.44
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Description: This book blends theory and applications, reinforcing concepts with practical real-world examples that illustrate the importance of probability to those who will use it in their subsequent courses and careers. The author emphasizes the study of  More...

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Book details

List price: $269.95
Copyright year: 2012
Publisher: Brooks/Cole
Publication date: 7/21/2011
Binding: Hardcover
Pages: 608
Size: 7.75" wide x 9.50" long x 1.25" tall
Weight: 2.618
Language: English

This book blends theory and applications, reinforcing concepts with practical real-world examples that illustrate the importance of probability to those who will use it in their subsequent courses and careers. The author emphasizes the study of probability distributions that characterize random variables, because this knowledge is essential in performing parametric statistical analysis. Explanations include the "why" as well as the "how" of probability distributions for random variables to help engage readers and further promote their understanding. In addition, the text includes a self-contained chapter on finite Markov chains, which introduces the basic aspects of Markov chains and illustrates their usefulness with several real examples.

Probability
Definition of Probability
Axiomatic Definition of Probability
Conditional Probability
Marginal Probabilities
Bayes' Theorem
Independent Events
Combinatorial Probability
Discrete Probability Distributions
Discrete Probability Density Function
Cumulative Distribution Function
The Point Binomial Distribution
The Binomial Probability Distribution
The Poisson Probability Distribution
The Hypergeometric Probability Distribution
The Geometric Probability Distribution
The Negative Binomial Probability Distribution
Probability Distributions of Continuous Random Variables
Continuous Random Variable and Probability Density Function
Cumulative Distribution Function of a Continuous Random Variable
The Continuous Probability Distributions
Functions of a Random Variable
Introduction
Distribution of a Continuous Function of a Discrete Random Variable
Distribution of a Continuous Function of a Continuous Random Variable
Other Types of Derived Distributions
Expected Values, Moments and Moment Generating Functions
Mathematical Expectation
Properties of Expectation
Moments
Moment Generating Function
Two Random Variables
Joint Probability Density Function
Bivariate Cumulative Distribution Function
Marginal Probability Distributions
Conditional Probability Density and Cumulative Distribution Functions
Independent Random Variables
Function of Two Random Variables
Expected Value and Moments
Conditional Expectation
Bivariate Normal Distribution
Sequence of Random Variables
Multivariate Probability Density Functions
Multivariate Cumulative Distribution Functions
Marginal Probability Distributions
Conditional Probability Density and Cumulative Distribution Functions
Sequence of Independent Random Variables
Functions of Random Variables
Expected Value and Moments
Conditional Expectation
Limit Theorems
Chebyshev's Inequality
Bernoulli's Law of Large Numbers
Weak and Strong Laws of Large Numbers
The Central Limit Theorem
The De Moivre-Laplace Theorem
Normal Approximation to the Poisson Distribution
Normal Approximation to the Gamma Distribution
Finite Markov Chains
Basic Concepts
N-Step Transitions Problems
Evaluation of Pn
Classification of States
Appendix
Index

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