Introductory Statistics, 4ed（统计学导论，Sheldon M. Ross，2017年第4版）

经典教材的详细答案，让你学习不在苦恼，不在彷徨，让你赢在起跑线上。

Solution.Manual.to.Introduction.to.Mathematical.statistics. Hogg..McKean.and.Craig

数理统计学导论-英文原版 含最新的第7和第8版。带书签目录。 Introduction to Mathematical Statistics (8th Edition) (What's New in Statistics)Jan 20, 2018 by Robert V. Hogg and Joseph W. McKean

1) 应用随机过程概率模型导论 第11版 [（美）SHELDON M.ROSS著2) 英文原版, PDF书签带目录。本书是国际知名统计学家Sheldon M. Ross所著的关于基础概率理论和随机过程的经典教材，被加州大学伯克利分校、哥伦比亚大学、普度大学、密歇根大学、俄勒冈州立大学、华盛顿大学等众多国外知名大学所采用。与其他随机过程教材相比，本书非常强调实践性，内含极其丰富的例子和习题，涵盖了众多学科的各种应用。作者富于启发而又不失严密性的叙述方式，有助于使读者建立概率思维方式，培养对概率理论、随机过程的直观感觉。对那些需要将概率理论应用于精算学、计算机科学、管理学和社会科学的读者而言，本书是一本极好的教材或参考书。第11版新增大量例子和习题，还对连续时间的马尔可夫链、漂移布朗运动等内容做了修订，更加注重强化读者的概率直观。============================Introduction to Probability Models, Eleventh Edition is the latest version of Sheldon Ross's classic bestseller, used extensively by professionals and as the primary text for a first undergraduate course in applied probability. The book introduces the reader to elementary probability theory and stochastic processes, and shows how probability theory can be applied fields such as engineering, computer science, management science, the physical and social sciences, and operations research.

概率论与数理统计经典教材Probability And Statistics For Engineering And The Sciences (Jay L. Devore) 5th Ed. - Solution Manual 答案

Statistics, 4th Edition by David Freedman统计学中经典之作

第三版次序统计，科研人员必不可少的经典书籍！作者H.A.David & H.N.Nagaraja

非常详细的一本统计计算的教材，虽然是英文版，但是数学上专业词汇也就那些，非常好懂，相信大家也都知道。matlab在统计计算中的应用应该是非常广泛，通过每一个统计计算的实例用matlab来实现，来进行学习，可以说效果非常好。

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. I believe that any machine learning practitioner should be proficient in statistics as well as in mathematics, so that they can speculate and solve any machine learning problem in an efficient manner. In this book, we will cover the fundamentals of statistics and machine learning, giving you a holistic view of the application of machine learning techniques for relevant problems. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on. We will also go over the fundamentals of deep learning with the help of Keras software. Furthermore, we will have an overview of reinforcement learning with pure Python programming language. The book is motivated by the following goals: To help newbies get up to speed with various fundamentals, whilst also allowing experienced professionals to refresh their knowledge on various concepts and to have more clarity when applying algorithms on their chosen data. To give a holistic view of both Python and R, this book will take you through various examples using both languages. To provide an introduction to new trends in machine learning, fundamentals of deep learning and reinforcement learning are covered with suitable examples to teach you state of the art techniques.