Random Processes for Engineers: A Primer [Kõva köide]

"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 WienerKhintchine 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 "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 WienerKhintchine 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