Pierre Moulin

Statistical Inference for Engineers and Data Scientists (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers and professionals. With a wealth of illustrations and examples to explain the key features of the theory and to connect with real-world applications, additional material to explore more advanced concepts, and numerous end-of-chapter problems to test the reader's knowledge, this textbook is the 'go-to' guide for learning about the core principles of statistical inference and its application in engineering and data science. The password-protected solutions manual and the image gallery from the book are available online.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 22. November 2018
• Englisch
• ISBN-13: 9781316952917
• Artikelnr.: 54669593

Autorenporträt

Pierre Moulin is a professor in the ECE Department at the University of Illinois, Urbana-Champaign. His research interests include statistical inference, machine learning, detection and estimation theory, information theory, statistical signal, image, and video processing, and information security. Moulin is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and served as a Distinguished Lecturer for the IEEE Signal Processing Society. He has received two best paper awards from the IEEE Signal Processing Society and the US National Science Foundation CAREER Award. He was founding Editor-in-Chief of the IEEE Transactions on Information Security and Forensics.

Inhaltsangabe

1. Introduction; Part I. Hypothesis Testing: 2. Binary hypothesis testing; 3. Multiple hypothesis testing; 4. Composite hypothesis testing; 5. Signal detection; 6. Convex statistical distances; 7. Performance bounds for hypothesis testing; 8. Large deviations and error exponents for hypothesis testing; 9. Sequential and quickest change detection; 10. Detection of random processes; Part II. Estimation: 11. Bayesian parameter estimation; 12. Minimum variance unbiased estimation; 13. Information inequality and Cramer-Rao lower bound; 14. Maximum likelihood estimation; 15. Signal estimation.

Rezensionen

'This book presents a rigorous and comprehensive coverage of the concepts underlying modern statistical inference, and provides a lucid exposition of the fundamental concepts. A distinguishing feature of the book is the large number of thoughtfully constructed examples, which go a long way towards aiding the reader in understanding and assimilating the concepts. As no particular domain expertise is assumed other than probability theory, the book should be widely accessible to a broad readership.' Kannan Ramchandran, University of California, Berkeley