Statistical Inference for Engineers and Data Scientists
Statistical Inference for Engineers and Data Scientists
Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign
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NZD$129.95
inc GSTThis 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.
- Presents the core principles of statistical inference in a unified manner which were previously only available piecemeal, particularly those involving large sample sizes
- The book is mathematically accessible, and provides plenty of examples to illustrate the concepts explained and to connect the theory with practical applications
- Contains a wealth of illustrations to emphasize the key features of the theory, the implications of the assumptions made, and the subtleties that arise when applying the theory
Reviews & endorsements
'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
'A wide-ranging, rigorous, yet accessible account of hypothesis testing and estimation, the pillars of statistical signal processing, communications, and data science at large.' Tsachy Weissman, STMicroelectronics Chair, Founding Director of the Stanford Compression Forum, Stanford University, California
Product details
November 2018Hardback
9781107185920
418 pages
258 × 177 × 23 mm
0.98kg
Available
Table of Contents
- 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.
