Probability for Engineering, Mathematics and Science, 1e

Tsokos, Chris P

ISBN 10: 1111430276 / ISBN 13: 9781111430276

Published by Thomson Brooks/Cole, 2011

About this Item

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Seller Inventory # ABBB-68766

Bibliographic Details

• Title: Probability for Engineering, Mathematics and Science, 1e
• Publisher: Thomson Brooks/Cole
• Publication Date: 2011
• Language: English
• ISBN 10: 1111430276
• ISBN 13: 9781111430276
• Binding: Soft cover
• Edition: International Edition
• Condition: New

About this title

Synopsis

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.

Review

1. PROBABILITY. Definition of Probability. Axiomatic Definition of Probability. Conditional Probability. Marginal Probabilities. Bayes' Theorem. Independent Events. Combinatorial Probability. 2. 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. 3. 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. 4. 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. 5. EXPECTED VALUES, MOMENTS AND MOMENT GENERATING FUNCTIONS. Mathematical Expectation. Properties of Expectation. Moments. Moment Generating Function. 6. 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. 7. 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. 8. LIMIT THEOREMS. Chebyshev's Inequality. Bernoulli's Law of Large Numbers. Weak and Strong Laws of Large Numbers. The Central Limit Theorem. The DeMoivre-Laplace Theorem. Normal Approximation to the Poisson Distribution. Normal Approximation to the Gamma Distribution. 9. FINITE MARKOV CHAINS. Basic Concepts. N-Step Transitions Problems. Evaluation of Pn. Classification of States. Appendix. Index.