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Probability for Information Technology

This book introduces probabilistic modelling and to study its role in solving a wide variety of engineering problems that arise in Information Technology (IT). The book consists of three parts. The first introduces the basic concepts of probability: sample space, events, conditional probability, ind...

詳細記述

保存先:
書誌詳細
第一著者: Suh, Changho (著者)
団体著者: SpringerLink (Online service)
フォーマット: 電子媒体 eBook
言語:英語
出版事項: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
版:1st ed. 2025.
主題:
Computer science > Mathematics.
Mathematical statistics.
Digital media.
Artificial intelligence > Data processing.
Machine learning.
Probability and Statistics in Computer Science.
Digital and New Media.
Data Science.
Machine Learning.
オンライン･アクセス:https://doi.org/10.1007/978-981-97-4032-1
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目次:
  • Preface
  • Acknowledgements
  • Part I. Basic concepts of probability
  • Chapter 1. Overview of the book
  • Chapter 2. Sample space and events
  • Chapter 3. Monty Hall problem and Python implementation
  • Problem Set 1
  • Chapter 4. Conditional probability and total probability law
  • Chapter 5. Independence
  • Chapter 6. Coupon collector problem and Python implementation
  • Problem Set 2
  • Chapter 7. Random variables
  • Chapter 8. Expectation
  • Chapter 9. BitTorrent and Python implementation
  • Chapter 10.Variance and Chebyshev’s inequality
  • Problem Set 3
  • Chapter 11.Continuous random variables
  • Chapter 12. Gaussian random variables
  • Problem Set 4
  • Part II. Introductory random processes and key principles
  • Chapter 13. Introduction to random processes
  • Chapter 14. Maximum A Posteriori (MAP) principle
  • Chapter 15. MAP: Multiple observations
  • Chapter 16. MAP: Performance analysis
  • Chapter 17. MAP: Cancer prediciton and Python implementation
  • Problem Set 5
  • Chapter 18. Maximum Likelihood Estimation (MLE)
  • Chapter 19. MLE: Law of large numbers
  • Chapter 20. MLE: Gaussian distribution
  • Chapter 21. MLE: Gaussian distribution estimation and Python implementation
  • Chapter 22. Central limit theorem
  • Problem Set 6
  • Part III. Information Technology Applications
  • Chapter 23. Communication: Probabilistic modeling
  • Chapter 24. Communication: MAP principle
  • Chapter 25. Communication: MAP under multiple observations
  • Chapter 26. Communication: Repetition coding and Python implementation
  • Problem Set 7
  • Chapter 27. Social networks: Probabilistic modeling
  • Chapter 28. Social networks: ML principle
  • Chapter 29. Social networks: Community detecition and Python implementation
  • Problem Set 8
  • Chapter 30. Speech recognition: Probabilistic modeling
  • Chapter 31. Speech recognition: MAP principle
  • Chapter 32. Speech recognition: Viterbi algorithm
  • Chapter 33. Speech recognition: Python implementation
  • Problem Set 9
  • Appendix A: Python basics
  • Bibliography
  • Index.

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