信号检测的经典教材，steven M.key的扛鼎之作，诚心推荐，希望对大家有用

不可多得的，全面的讲解各种概率分布的手册。主要用于科研实验人员使用时查询

Goodman J.W. Statistical Optics英文教材分享

《The Elements of Statistical Learning-Data Mining, Inference, and Prediction》英文原版教材第二版

自己通过剪裁制作的单页显示版本，更加适合阅读。

统计学习(the element of statistical learning英文原版)

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s resea

Aspects of multivariate statistical theory, 高清

Focusing on implementation rather than theory, 'Statistical Computing with R' serves as a valuable tutorial, providing examples that illustrate programming concepts in the context of practical computational problems. This book presents an overview of computational statistics with an introduction to the R computing environment. Reviewing basic concepts in probability and classical statistical inference, the text demonstrates every algorithm through fully implemented examples coded in R. Chapters cover topics such as Monte Carlo methods, clustering, bootstrap, nonparametric regression, density estimation, and goodness-of-fit. Many exercises are included for the students while a solutions manual is included for the instructor.

这本书比Van Trees的书成书要晚，所以内容比较新。作者的作风很严谨，书中的推导极其严密。不失为一位严谨的学者的作风！虽说推导严密，但是本书 也不只是单纯讲数学的，与工程应用也很贴近。这就是本书的特点。这两册书是统计信号之集大成者。有志于这个领域的，此书必备。