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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.

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



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.