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Book Synopsis

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.

Probability for Information Technology

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    A Hardback by Changho Suh

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      Publisher: Springer
      Publication Date: Publication Date: 18/11/2024
      ISBN13: 9789819740314, 978-9819740314
      ISBN10:

      Description

      Book Synopsis

      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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