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 Sergio Rui Silva - InTech
Simulation and 3D reconstruction of side-looking sonar images, synthetic aperture techniques, ensemble averaging and resolution enhancement of digital radar and sonar signals, multi-sonar integration and the advent of sensor intelligence, and more.
by R. J. Elliott, L. Aggoun, J. B. Moore - Springer
The aim of this book is to present graduate students with a thorough survey of reference probability models and their applications to optimal estimation and control. Readers are assumed to have basic grounding in probability and systems theory.
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.
- Agilent Technologies
This text is a primer for those who are unfamiliar with the advantages of analysis in the frequency and modal domains and Dynamic Signal Analyzers. The authors avoid the use of rigorous mathematics and instead depend on heuristic arguments.