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.
Home page url
Download or read it online for free here:
(multiple PDF files)
by Allen B. Downey - Green Tea Press
'Think DSP: Digital Signal Processing in Python' is an introduction to signal processing and system analysis using a computational approach. The premise of this book is that if you know how to program, you can use that skill to learn other things.
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 G. Larry Bretthorst - Springer
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.
by Julius O. Smith III - W3K Publishing
Detailed mathematical derivation of DFT (Discrete Fourier Transform), with elementary applications to audio signal processing. Matlab programming examples are included. High-school math background is a prerequisite, including some calculus.