Probability Basics. Random Processes. Analysis of Raw Data. Models for Random Processes. Least Mean-Square Error Predictors. The Kalman Filter.
Dr. Arthur David Snider has over fifty years of experience in modeling physical systems in the areas of heat transfer, electromagnetics, microwave circuits, and orbital mechanics, as well as the mathematical areas of numerical analysis, signal processing, differential equations, and optimization. He holds degrees in both mathematics (BS, MIT, PhD, NYU) and physics (MA, Boston U), and he is a registered professional engineer. He served for forty-five years on the faculties of mathematics, physics, and electrical engineering at the University of South Florida after working for five years as a systems analyst at MIT's Draper Instrumentation Lab. He consults in many industries in Florida and has published five other textbooks in applied mathematics.
"This is great and timely book! It takes difficult concepts and
distills them to the reader in a way that is simple and easy to
understand. It connects students with hard to understand theories
and concepts though the use of good examples and graphical
illustrations."
— George Edwards, University of Denver, USA"This book offers an
intuitive approach to random processes and discusses how to
interpret and predict their behavior. Based on the idea that new
techniques are best introduced by specific, low-dimensional
examples, the mathematical exposition is made easier to comprehend
and serves to motivate the subsequent generalizations. It
distinguishes between the science of extracting statistical
information from raw data such as a time series about which nothing
is known a priori and that of analyzing specific statistical
models, such as Bernoulli trials, Poisson queues, ARMA, and Markov
processes. The former motivates the concepts of statistical
spectral analysis (such as the Wiener–Khintchine theory), and the
latter applies and interprets them in specific physical contexts.
The Kalman filter is introduced in a simple scalar context, where
its basic strategy is transparent and gradually extended to the
full-blown iterative matrix form."
—IEEE Control Systems Magazine, December 2017 Issue
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