Filtering and prediction: a primer

The authors stick to the basics in describing the process of observing moving objects when the observations are corrupted by random errors, filtering out the errors and extracting the most precise information possible, and then predicting the future path of the object from past behavior. They do so as accessibly as possible without significantly reducing rigor, giving students a solid background in such prerequisites for studying filtering and prediction as series, probability concepts and conditioning in the discrete case. With plenty of examples and exercises, they cover Markov chains, filtering of discrete Markov chains, conditional expectations, filtering of continuous-space Markov chains, Wiener process and continuous time filtering, stationary sequences and prediction of stationary sequences. The result is a handy guide not only for students but for nonspecialist professionals or those who need to review basic concepts. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)