This text is intended primarily for readers interested in mathematical probability as applied to mathematics, statistics, operations research, engineering, and computer science. It is also appropriate for mathematically oriented readers in the physical and social sciences. Prerequisite material consists of basic set theory and a firm foundation in elementary calculus, including infinite series, partial differentiation, and multiple integration. Some exposure to rudimentary linear algebra (e.g., matrices and determinants) is also desirable. This text includes pedagogical techniques not often found in books at this level, in order to make the learning process smooth, efficient, and enjoyable. Fundamentals of Probability: Probability Basics. Mathematical Probability. Combinatorial Probability. Conditional Probability and Independence.Discrete Random Variables: Discrete Random Variables and Their Distributions. Jointly Discrete Random Variables. Expected Value of Discrete Random Variables.Continuous Random Variables: Continuous Random Variables and Their Distributions. Jointly Continuous Random Variables. Expected Value of Continuous Random Variables.Limit Theorems and Advanced Topics: Generating Functions and Limit Theorems. Additional Topics. For all readers interested in probability.This text is intended primarily for readers interested in mathematical probability as applied to mathematics, statistics, operations research, engineering, and computer science.
|Title||:||A Course in Probability|
|Author||:||Neil A. Weiss, Paul T. Holmes, Michael Hardy|
|Publisher||:||Pearson College Division - 2006|