Probability in Electrical Engineering and Computer Science An Application-Driven Course
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, includin...
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フォーマット: | 電子媒体 図書の章 |
言語: | 英語 |
出版事項: |
Springer Nature
2021
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主題: | |
オンライン・アクセス: | OAPEN Library: download the publication OAPEN Library: description of the publication |
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要約: | 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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物理的記述: | 1 electronic resource (380 p.) |
ISBN: | 978-3-030-49995-2 9783030499952 |
アクセス: | Open Access |