数据可视化与分析 我花时间用R编程进行探索性数据分析。 这些问答教程在#Kaggle上使用了儿童自闭症数据

BGGM：贝叶斯高斯图形模型 R包BGGM提供了用于在高斯图形模型（GGM）中进行贝叶斯推理的工具。 这些方法围绕用于贝叶斯推断的两种通用方法进行组织：（1）估计和（2）假设检验。 关键区别在于，前者着眼于后验或后验预测分布（Gelman，Meng和Stern，1996年；见Rubin 1984年的第5节），而后者着眼于与贝叶斯因子的模型比较（Jeffreys 1961年； Kass and Raftery（1995）。 什么是高斯图形模型？ 高斯图形模型捕获了一组变量之间的条件（非）依赖关系。 这些是成对关系（部分相关性），用于控制模型中所有其他变量的影响。 应用领域 高斯图形模型被用于各种科学领域，包括（但不限于）经济学（Millington和Niranjan 2020），气候科学（Zerenner等人，2014），遗传学（Chu等人，2009）和心理学（Rodriguez等人，

时间序列分析——The main focus of this book is on a systematic development of the theory of sequential hypothesis testing (Part I) and changepoint detection (Part II). In Part III, we briefly describe certain important applications where theoretical results can be used efficiently, perhaps with some reasonable modifications. We review recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The emphasis is not only on more traditional binary hypotheses but also on substantially more difficult multiple decision problems. Scenarios with simple hypotheses and more realistic cases of (two and finitely many) composite hypotheses are considered and treated in detail. While our major attention is on more practical discrete-time models, since we strongly believe that life is discrete in nature??? (not only due to measurements obtained from devices and sensors with discrete sample rates), certain continuous-timemodels are also considered once in a while, especially when general results can be obtained very similarly in both cases. It should be noted that although we have tried to provide rigorous proofs of the most important results, in some cases we included heuristic argument instead of the real proofs as well as gave references to the sources where the proofs can be found.

Jobson, J. , & Korkie, B. (1981). Performance hypothesis testing with the Sharpe and Treynor measures. Journal of Finance, 36 (4), 889–908 .