七夕特惠，新书预告。当代统计学的两位大师，斯坦福教授Bradley Efron和Trevor Hastie出了新书《计算机时代下的统计推断：算法，证据和数据科学》，内容是关于计算机时代下的大数据统计推断，Efron出的书每本都是经典。

书的网站是http://web.stanford.edu/~hastie/CASI

前言和目录http://assets.cambridge.org/97811071/49892/frontmatter/9781107149892_frontmatter.pdf

第1章http://assets.cambridge.org/97811071/49892/excerpt/9781107149892_excerpt.pdf

第6章 http://statweb.stanford.edu/~ckirby/brad/other/CASI_Chap6_Nov2014.pdf
第7章 http://statweb.stanford.edu/~ckirby/brad/other/CASI_Chap7_Nov2014.pdf

第9章 http://statweb.stanford.edu/~ckirby/brad/other/CASI_Chap9_Nov2014.pdf

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)Sep 30, 2016 by Bradley Efron and Trevor Hastie

https://www.amazon.com/gp/product/1107149894

The twenty first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.二十一世纪以来的统计方法以一个惊人的速度扩张，无论是在范围和影响力。 “大数据”，“数据科学”和“机器学习”已成为新闻熟悉的术语，现代科学和商业带来的巨大数据集促使统计方法的发展。我们怎么会在这里？而我们要去哪里？继20世纪50年代引入的电子计算机，通过革命的数据分析，这本书让我们在一个令人振奋的旅程中。 以古典统计推理理论开始，这包括贝叶斯学派，频率论，费雪学派，后面独立的章节采取了一系列有影响力的主题：生存分析，logistic回归，经验贝叶斯，刀切法和自助法，随机森林，神经网络，马尔可夫链蒙特卡罗，模型选择后的推断，等等方法。在不同的现代方法中，集成了统计推断的方法和算法。本书以统计和数据科学的未来发展方向的猜测结束。

下面是封面和目录,期待以后能找到完整pdf电子版。