Probability in Electrical Engineering and Computer Science

An Application-Driven Course
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Kurzbeschreibung des Verlags:

This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com.
This is an open access book. 

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ISBN 9783030499976
Sprache Englisch
Ausgabe 1st ed. 2021
Erscheinungsdatum 24.06.2022
Umfang 380 Seiten
Genre Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Format Taschenbuch
Verlag Springer International Publishing
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