Textbook on theory and application of probability and random processes (upper-level undergraduate)
|7 Dec 2009||MathWorks Classroom Resources Team||
"Intuitive Probability and Random Processes Using MATLAB", by Steven Kay. Springer, 2006.
Written for upper-level undergraduate and first-year graduate students in engineering, this text presents the theory and application of probability and random processes. Topics covered include conditional probability, discrete random variables, Gaussian and Poisson random processes, and Markov chains.
== Table of Contents
Introduction.- Computer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.