120 of 120 people found the following review helpful:
5.0 out of 5 stars
Not as basic as you might think, November 16, 2008
This review is from: Basic Probability Theory (Dover Books on Mathematics) (Paperback)
There is no "Look inside" displayed for this book, so here is a copy of the table of contents.
1. BASIC CONCEPTS
1.1 Introduction
1.2 Algebra of Events (Boolean Algebra)
1.3 Probability
1.4 Combinatorial Problems
1.5 Independence
1.6 Conditional Probability
1.7 Some Fallacies in Combinatorial Problems
1.8 Appendix: Stirling's Formula
2. RANDOM VARIABLES
2.1 Introduction
2.2 Definition of a Random Variable
2.3 Classification of Random Variables
2.4 Functions of a Random Variable
2.5 Properties of Distribution Functions
2.6 Joint Density Functions
2.7 Relationship Between Joint and Individual Densities;
Independence of Random Variables
2.8 Functions of More Than One Random Variable
2.9 Some Discrete Examples
3.EXPECTATION
3.1 Introduction
3.2 Terminology and Examples
3.3 Properties of Expectation
3.4 Correlation
3.5 The Method of Indicators
3.6 Some Properties of the Normal Distribution
3.7 Chebyshev's Inequality and the Weak Law of Large Numbers
4.CONDITIONAL PROBABILITY AND EXPECTATION
4.1 Introduction
4.2 Examples
4.3 Conditional Density Functions
4.4 Conditional Expectation
4.5 Appendix: The Generâl Concept of Conditional Expectation
5.CHARACTERISTIC FUNCTIONS
5.1 Introduction
5.2 Examples
5.3 Properties of Characteristic Functions
5.4 The Central Limit Theorem
6. INFINITE SEQUENCES OF RANDOM VARIABLES
6.1 Introduction
6.2 The Gambler's Ruin Problem
6.3 Combinatorial Approach to the Random Walk; the Reflection
Principle
6.4 Generating Functions
6.5 The Poisson Random Process
6.6 The Strong Law of Large Numbers
7. MARKOV CHAINS
7.1 Introduction
7.2 Stopping Times and the Strong Markov Property
7.3 Classification of States
7.4 Limiting Probabilities
7.5 Stationary and Steady-State Distributions
8. INTRODUCTION TO STATISTICS
8.1 Statistical Decisions
8.2 Hypothesis Testing
8.3 Estimation
8.4 Sufficient Statistics
8.5 Unbiased Estimates Based on a Complete Sufficient Statistic
8.6 Sampling from a Normal Population
8.7 The Multidimensional Gaussian Distribution
Tables
A Brief Bibliography
Solutions to Problems
This book is an excellent introduction that stops short of
doing a measure-theoretic treatment of probability theory, but it does
bring the concepts up. It is aimed at the person who likes a theoretic theorem-proof approach with all steps spelled out (again with the proviso that
it does not go into detail so far as constructing measures etc), so it does require more mathematical maturity than what you would expect in first year university students.
It might be called an "intermediate level course in probability theory".
When you have read this I recommend you move up to the same author's book
Probability & Measure Theory, Second Edition which is very clear too and generous with detail just like this one.
It seems obvious when reading this that the author loves teaching and is like a friend standing over your shoulder helping you.
There are 43 pages of solutions to problems, so this is very helpful.
My only niggle is that the author does not deal with hazard rates which are much used in engineering.
The value for money is fantastic.
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