Statistical Inference for Engineers and Data Scientists (Hardcover)
暫譯: 工程師與數據科學家的統計推斷 (精裝版)

Moulin, Pierre, Veeravalli, Venugopal V.

Statistical Inference for Engineers and Data Scientists (Hardcover)

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商品描述

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.

商品描述(中文翻譯)

這本書是一本數學上易於理解且與時俱進的介紹，涵蓋了解決現代工程和數據科學中的推斷問題所需的工具，特別適合修習統計推斷、檢測與估計課程的研究生，並且對於研究人員和專業人士來說是一本無價的參考書。書中包含大量插圖和範例，以解釋理論的關鍵特徵並與現實世界的應用相連結，還有額外的材料來探索更高級的概念，以及眾多的章末問題來測試讀者的知識，這本教科書是學習統計推斷核心原則及其在工程和數據科學中應用的「首選」指南。受密碼保護的解答手冊和書中的圖片庫可在線上獲得。

作者簡介

Pierre Moulin, University of Illinois, Urbana-Champaign
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.

Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign
Venugopal V. Veeravalli is the Henry Magnuski Professor in the ECE Department at the University of Illinois, Urbana-Champaign. His research interests include statistical inference and machine learning, detection and estimation theory, and information theory, with applications to data science, wireless communications and sensor networks. Veeravalli is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and served as a Distinguished Lecturer for the IEEE Signal Processing Society. Among the awards he has received are the IEEE Browder J. Thompson Best Paper Award, the National Science Foundation CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), and the Wald Prize in Sequential Analysis.

作者簡介(中文翻譯)

皮埃爾·穆蘭，伊利諾伊大學香檳分校

皮埃爾·穆蘭是伊利諾伊大學香檳分校電子與計算機工程系的教授。他的研究興趣包括統計推斷、機器學習、檢測與估計理論、信息理論、統計信號、影像及視頻處理，以及信息安全。穆蘭是電氣和電子工程師學會（IEEE）的會士，並曾擔任IEEE信號處理學會的特邀講者。他曾獲得IEEE信號處理學會的兩項最佳論文獎以及美國國家科學基金會的CAREER獎。他是IEEE信息安全與取證期刊的創始主編。

維努戈帕爾·V·維拉瓦利，伊利諾伊大學香檳分校

維努戈帕爾·V·維拉瓦利是伊利諾伊大學香檳分校電子與計算機工程系的亨利·馬格努斯基教授。他的研究興趣包括統計推斷和機器學習、檢測與估計理論以及信息理論，並應用於數據科學、無線通信和傳感器網絡。維拉瓦利是電氣和電子工程師學會（IEEE）的會士，並曾擔任IEEE信號處理學會的特邀講者。他所獲得的獎項包括IEEE布勞德·J·湯普森最佳論文獎、國家科學基金會的CAREER獎、總統早期科學家與工程師獎（PECASE）以及序列分析的瓦爾德獎。

目錄大綱

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.

目錄大綱(中文翻譯)

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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