This book offers an intuitive approach to random processes and educates the reader on how to interpret and predict their behavior.
Premised on the idea that new techniques are best introduced by specific, low-dimensional examples, the mathematical exposition
is easier to comprehend and more enjoyable, and it motivates the subsequent generalizations. It distinguishes between the
science of extracting statistical information from raw data--e.g., 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 formidable 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.