by Javier Prieto Tejedor (ed.)
Publisher: InTech 2017
Number of pages: 376
This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.
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by Carl W. Helstrom - Prentice Hall
This is an introduction to signal-detection theory, a subject fundamental to the design of detectors of weak signals in the presence of random noise, and to the design of optimal receivers of communication, radar, sonar and optical signals.
by J. H. Karl - Academic Press
The book comprises a one-semester or self-study course, filling the gap between several oversimplified introductions and more topically specialized or formal treatments. Karl's book wins notable points for its easy reading style.
by Jeff Fessler - University of Michigan
Course objectives: 1. to teach students the concepts of discrete-time signals, including mathematical representations; 2. to teach students the concepts of linear time-invariant discrete-time systems; 3. to introduce the concepts of filter design.
by Paolo Prandoni, Martin Vetterli - EFPL Press
The book is less focused on the mathematics and more on the concepts, allowing students to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics and helping students solve real-world problems.