Statistical Inference for Engineers and Data Scientists
$89.99 (X)
- Authors:
- Pierre Moulin, University of Illinois, Urbana-Champaign
- Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign
- Date Published: January 2019
- availability: Available
- format: Hardback
- isbn: 9781107185920
$
89.99
(X)
Hardback
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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.
Read more- 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
See more reviews'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
Customer reviews
17th Oct 2024 by UName-895727
This book introduces the readers to the theory of detection and estimation. A great advantage of this book is that it uses a lot of figures to illustrate theoretical concepts and intuitions, which make the materials easy to understand. The coverage of this book is comprehensive, and thus it is good for graduate students who are interested in statistical inference.
Review was not posted due to profanity
×Product details
- Date Published: January 2019
- format: Hardback
- isbn: 9781107185920
- length: 418 pages
- dimensions: 258 x 177 x 23 mm
- weight: 0.98kg
- availability: 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.
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