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 Brad Osgood - Stanford University
This text is appropriate for science and engineering students. Topics include: Periodicity and Fourier series; The Fourier transform and its basic properties; Convolution and its applications; Distributions and their Fourier transforms; etc.
by William A. Gardner - McGraw-Hill
A first course on random processes for graduate engineering and science students, particularly those with an interest in the analysis and design of signals and systems. The book includes detailed coverage of minimum-mean-squared-error estimation.
by Bruce Hajek - University of Illinois at Urbana-Champaign
These notes were written for a graduate course on random processes. Students are assumed to have had a previous course in probability, some familiarity with real analysis and linear algebra, and some familiarity with complex analysis.
by William A. Gardner - Prentice Hall
This book is intended to serve as both a graduate-level textbook and a technical reference. The focus is on fundamental concepts, analytical techniques, and basic empirical methods. The only prerequisite is an introductory course on Fourier analysis.