Probability in electrical engineering and computer science an application-driven course / [electronic resource] :

• 作者： Walrand, Jean.
• 其他作者：
  • SpringerLink (Online service)
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 主題： Probabilities. , Electrical engineering--Mathematics. , Computer science--Mathematics. , Probability and Statistics in Computer Science. , Communications Engineering, Networks. , Mathematical and Computational Engineering. , Probability Theory and Stochastic Processes. , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
• ISBN： 9783030499952 (electronic bk.) 、 9783030499945 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter 1. Page Rank - A -- Chapter 2. Page Rank - B -- Chapter 3. Multiplexing - A -- Chapter 4. Multiplexing - B -- Chapter 5. Networks - A -- Chapter 6. Networks - B -- Chapter 7. Digital Link - A -- Chapter 8. Digital Link - B -- Chapter 9. Tracking - A -- Chapter 10. Tracking - B -- Chapter 11. Speech Recognition - A -- Chapter 12. Speech Recognition - B -- Chapter 13. Route planning - A -- Chapter 14. Route Planning - B -- chapter 15. Perspective & Complements -- A. Elementary Probability -- B. Basic Probability -- Index.
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• 摘要註: 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. The companion website now has many examples of Python demos and also Python labs used in Berkeley. Showcases techniques of applied probability with applications in EE and CS; Presents all topics with concrete applications so students see the relevance of the theory; Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters.
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