Statistics on Special Manifolds，统计学的书 ，经典

Density Estimation for Statistics and Data Analysis， Silverman著， 1986年版，核密度估计教材

Review From the reviews: "Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004 "This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004 "Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005 "As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005) "This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004) "This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004) "The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004) "The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005) Product Description This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.

python统计数据分析

Python for Probability,Statistics,and Machine Learning.pdf Python for Probability,Statistics,and Machine Learning.pdf

Introduction to probability and statistics 概率与统计的入门书籍，适合自学

Probability+and+Statistics+for+Computer+Scientists

元数值 Meta.Numerics是一个用于.NET平台的高级数值计算库。 它提供了面向对象的API，用于数据处理，统计分析，高级功能，矩阵代数，傅立叶变换，高级功能，扩展精度算术和求解器功能，例如集成，优化和求根。 Meta.Numerics是David Wright的2008-2020年版权。 它是根据Microsoft公共许可证（BSD风格的开源许可证）获得许可的。 有关更多信息，请访问 。

金融占星术统计 自古代文明以来，人们观察到，当特定的行星循环重复发生时，自然又会发生一些与过去相似的世俗事件。 在公元前1800年注意到这种相关性的，我们在2021年，占星术仍在实践中，受到某人的爱戴，而另一些人则恨之入骨。 某些预测能力可能隐藏在行星周期的背后吗？ 好吧，让我们考虑一下...从统计学家和市场分析师的角度来看，完全可以接受可能存在可以预测价格的季节性影响。 正确的？ 通常在时间序列中，按Wikipedia页面中的说明，按季节，按月，按周，按季度等来模拟。 如果您对此进行考虑，您可能会问：一年，一个月或一天是什么？ 这只是时间度量，但结果是这些度量与行星有关：我们的年份是地球经度位置与太阳的关系。 我们的月份大约是28天的月球自转周期，而我们的24小时（昼/夜）是地球自转周期。 最后，我们的日子名称与某些行星的名称相似，并且有其意图，如《维基百科页面所述。 阿兹台克人也有一

机器学习的时间序列预测 一组预测时间序列的不同机器学习模型，具体来说是给定货币图表和目标的市场价格。 要求 必需的依赖项： numpy 。 其他依赖项是可选的，但是为了使最终模型更多样化，建议安装以下软件包： tensorflow ， xgboost 。 经过python版本测试：2.7.14、3.6.0。 取得资料 有一个内置的数据提供程序，可以从获取数据。 目前，所有模型都已通过加密货币图表进行了测试。 提取的数据格式是标准安全性：日期，最高，最低，打开，关闭，交易量，报价量，weightedAverage。 但是模型与特定的时间序列特征无关，并且可以使用这些特征的子集或超集进行训练。 要获取数据， 从根目录运行脚本： # Fetches the default tickers: BTC_ETH, BTC_LTC, BTC_XRP, BTC_ZEC for all time periods. $ ./run_fetch.py 默认情况下，将提取Poloniex中所有可用时间段（天，4h，2h，30m，15m，5m）的数据，并将其存储在_data目录中。 您可以通过命令行参