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020 _a9783030499952
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024 7 _a10.1007/978-3-030-49995-2
_2doi
050 4 _aQA76.9.M35
050 4 _aQA276-280
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082 0 4 _a004.0151
_223
100 1 _aWalrand, Jean.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aProbability in Electrical Engineering and Computer Science
_h[electronic resource] :
_bAn Application-Driven Course /
_cby Jean Walrand.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXXI, 380 p. 214 illus., 146 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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.
506 0 _aOpen Access
520 _aThis 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.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 0 _aTelecommunication.
650 0 _aEngineering mathematics.
650 0 _aEngineering
_xData processing.
650 0 _aProbabilities.
650 0 _aStatistics .
650 1 4 _aProbability and Statistics in Computer Science.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aMathematical and Computational Engineering Applications.
650 2 4 _aProbability Theory.
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030499945
776 0 8 _iPrinted edition:
_z9783030499969
776 0 8 _iPrinted edition:
_z9783030499976
856 4 0 _uhttps://doi.org/10.1007/978-3-030-49995-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
999 _c37402
_d37402