Probability in Electrical Engineering and Computer Science

An Application-Driven Course
Artikelnummer: 978-3-030-49997-6
Einband: Kartonierter Einband (Kt)
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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.

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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VerlagSpringer
EinbandKartonierter Einband (Kt)
Erscheinungsjahr2022
Seitenangabe408 S.
AusgabekennzeichenEnglisch
MasseH23.5 cm x B15.5 cm x D2.3 cm 616 g
AutorWalrand, Jean

Über den Autor Jean Walrand

Jean Walrand received his Ph.D. in EECS from UC Berkeley, and has been on the faculty of that department since 1982. He is the author of An Introduction to Queueing Networks (Prentice Hall, 1988), Communication Networks: A First Course (2nd ed., McGraw-Hill, 1998), and Probability in Electrical Engineering and Computer Science (Amazon, 2014), and co-author of High-Performance Communication Networks (2nd ed., Morgan Kaufman, 2000), Scheduling and Congestion Control for Communication and Processing Networks (Morgan & Claypool, 2010), and Sharing Network Resources (Morgan & Claypool, 2014). His research interests include stochastic processes, queuing theory, communication networks, game theory, and the economics of the Internet. Prof. Walrand is a Fellow of the Belgian American Education Foundation and of the IEEE, and a recipient of the Informs Lanchester Prize, the IEEE Stephen O. Rice Prize, the IEEE Kobayashi Award, and the ACM Sigmetrics Achievement Award. Shyam Parekh received his Ph.D. in EECS from UC Berkeley in 1986. He is currently an Associate Adjunct Professor in the EECS department at UC Berkeley. He has previously worked at AT&T Labs, Bell Labs, TeraBlaze, and ConSentry Networks. He was a co-chair of the Application Working Group of the WiMAX Forum during 2008. He is a co-editor of Quality of Service Architectures for Wireless Networks (Information Science Reference, 2010). He currently holds 10 U.S. patents. His research interests include architecture, modeling, and analysis of both wired and wireless networks.

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