matlab copula代码统计 - Matlab 静态和概率相关的自制 Matlab 函数的集合。 功能按主题分类并放置在相关文件夹中。 内容亮点 许多具有最大似然拟合的单变量分布：对数正态、Weibull、logPearson 等。 二元分布函数，主要是 copulas：Clayton、Gumbel、normal、Hüsler-Reiss、tev 等。 扩展 Kolmogorov-Smirnov 检验 贝叶斯统计工具：可信区间和等高线 最大似然：轮廓诊断，观察到的 Fisher 信息矩阵 简单的直方图（不依赖于任何工具箱） 随机变量代数：两个独立随机变量的和和乘积 等等。 笔记 说明可以在源代码中作为注释找到 代码已经过测试，但不广泛 任何反馈，感谢贡献 致谢 本 repo 中的脚本是在布达佩斯科技经济大学开发的。

Brase's Understanding Basic Statistics Edition 6

Matlab统计与机器学习工具箱官方用户指导，是mathwork公司出版的用于指导用户使用Matlab机器学习工具箱的官方文档，对于需要利用Matlab来进行机器学习的情况下，参考官方文档是很有帮助的，文档全英文，共有8000多页

生存分析最初是由精算和医学界开发并广泛应用的。 其目的是回答为什么现在发生事件而不是后来在不确定性下发生（事件可能指死亡，疾病缓解等）。 这对于对测量寿命感兴趣的研究人员非常有用：他们可以回答诸如哪些因素可能影响死亡的问题？ 但是，除了医学和精算科学外，生存分析还有许多其他有趣而激动人心的应用。 例如： SaaS提供商对衡量订户的生存期或采取某些第一行动的时间感兴趣 库存缺货是对商品真正“需求”的审查事件。 社会学家对衡量政党的一生，人际关系或婚姻感兴趣 A / B测试可确定不同组执行一项操作需要多长时间。 lifelines是生存分析的最佳部分的纯Python实现。 记录和生存分析简

This introduction to biostatistics and measurement is the first in a series of articles designed to provide Radiology readers with a basic understanding of statistical concepts. Although most readers of the radiology literature know that application of study results to their practice requires an understanding of statistical issues, many may not be fully conversant with how to interpret statistics. The goal of this series is to enhance the ability of radiologists to evaluate the literature competently and critically, not make them into statisticians.

[美国大学教材] 基础统计学 第十版 Elementary Statistics 10e

Judea Pearl的因果推断教材，因果图模型

美国部分大学CS专业所学的概率统计用书 带详细案例和大量例题 附上自己做的例题答案，可能部分有错误

probability and statistics for engineering and the sciences (eighth edition) JAY L. DEVORE

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 tech niques 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.