LEADER 06329nam 22006135 450 001 9910983060503321 005 20241117120810.0 010 $a9789819740321$b(electronic bk.) 010 $z9789819740314 024 7 $a10.1007/978-981-97-4032-1 035 $a(MiAaPQ)EBC31785794 035 $a(Au-PeEL)EBL31785794 035 $a(CKB)36601345800041 035 $a(DE-He213)978-981-97-4032-1 035 $a(EXLCZ)9936601345800041 100 $a20241117d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProbability for Information Technology /$fby Changho Suh 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (359 pages) 311 08$aPrint version: Suh, Changho Probability for Information Technology Singapore : Springer,c2024 9789819740314 327 $aPreface -- 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. 330 $aThis 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, independence, total probability law, random variables, probability mass functions, density functions and expectation. In the second part, we study the concept of random processes, as well as key principles such as Maximum A Posteriori (MAP) estimation, Maximum Likelihood (ML) estimation, law of large numbers and central limit theorem. Using the language and principles acquired in the prior parts, the last discusses IT applications chosen from communication, social networks and speech recognition. The book puts a special emphasis on ?probability in action?: probabilistic concepts are taught through many running examples, killer applications and Python coding exercises. One defining feature of this book is that it succinctly relates the ?story? of how the key principles of probability play a role, via classical and trending IT applications. All the key ?plots? involved in the story are coherently developed with the help of tightly-coupled exercise problem sets, and the associated fundamentals are explored mostly from first principles. Another key feature is that it includes programming implementation of toy examples and various algorithms inspired by fundamentals. It also provides a brief tutorial of the used programming tool: Python. This book does not follow a traditional book-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent storylines and themes. It serves as a textbook mainly for a sophomore-level undergraduate course, yet is also suitable for a junior or senior-level undergraduate course. Readers benefit from having some mathematical maturity and exposure to programming. But the background can be supplemented by almost self-contained materials, as well as by numerous exercise problems intended for elaborating on non-trivial concepts. In addition, Part III for IT applications should provide motivation and insights to students and even professional engineers who are interested in the field. 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aDigital media 606 $aArtificial intelligence$xData processing 606 $aMachine learning 606 $aProbability and Statistics in Computer Science 606 $aDigital and New Media 606 $aData Science 606 $aMachine Learning 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aDigital media. 615 0$aArtificial intelligence$xData processing. 615 0$aMachine learning. 615 14$aProbability and Statistics in Computer Science. 615 24$aDigital and New Media. 615 24$aData Science. 615 24$aMachine Learning. 676 $a004.015192 700 $aSuh$b Changho$01310449 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910983060503321 996 $aProbability for Information Technology$94317144 997 $aUNINA