贝叶斯matlab代码实例贝叶斯在线多元变化点检测算法 学生： Ilaria Lauzana 主管： ，何塞·梅迪纳（Jose Medina） 该存储库包含由Ilaria Lauzana，Nadia Figueroa和Jose Medina提出的贝叶斯在线多元变化点检测算法的实现。 我们提供3种实现： Matlab的 Python ros节点从流数据中检测变更点（online_changepoint_detector） 您可以在相应的文件夹中找到每个实现： 结构 . ├── README.md └── matlab ├── README.md │   └── code │   └── lightspeed └── python ├── python-univariate ├── README.md │   └── bayesian_changepoint_detection ├── python-multivariate └── online_changepoint_detector ├── CMakeLists.txt ├── package.xml └── scripts └──

MATLAB用拟合出的代码绘图变更点检测和理想观察者分析 该存储库包含一个工具箱，用于对IBL任务进行模型拟合和理想观察者模型的绘制。 目前，该代码仅在Matlab中可用。 安装指南 或在本地计算机上克隆存储库。 将存储库的基本文件夹（包含ibl_changepoint_add2path.m文件）添加到Matlab路径。 警告：不建议将整个存储库树永久添加到您的Matlab路径，因为这可能导致函数名称与其他项目冲突。 如果尚未安装它们，则从中安装Bayesian自适应直接搜索（BADS；优化工具箱），从中安装Variational Bayesian Monte Carlo （VBMC；近似后验工具箱）。 使用（将需要IBL访问凭据）将示例小鼠的IBL数据下载到data文件夹中的CSV文件中。 基本概述 举一个简单的例子，我们假设一个“无所不知”的贝叶斯观测器适合鼠标稳定会话数据，并假设存在偏差（请参见下面的说明）。 示例小鼠是CSHL_005 ， CSHL_007 ， IBL-T1 ， IBL-T4 ， ibl_witten_04 ， ibl_witten_05 ; 我们将在这里适合第

matlab-changepoint-分析 参考 CPR 算法最初描述于： 格雷格·詹森 (2013)。 多个变化点模型的封闭形式估计。 PeerJ 预印本 1:e90v3 引文 要引用包matlab-changepoint-analysis ，请使用： 格雷格·詹森 (2014)。 matlab 变化点分析。 Matlab 软件包版本 0.0.1。 LaTeX 用户可以使用以下 BibTeX 条目 @Manual{, title = {matlab-changepoint-analysis}, author = {Greg Jensen}, year = {2014}, note = {Matlab package version 0.0.1}, url = { , }

pydata-bayes-changepoint Python 中的变点检测算法 在 iPython notebook (Python v.2.7) 中工作正常

多元数据的非参数多变化点检测 在这个项目中，我从下面的出色非参数变化点检测论文中提供了除法算法的python实现。 Matteson, David S., and Nicholas A. James. "A nonparametric approach for multiple change point analysis of multivariate data." Journal of the American Statistical Association 109.505 (2014): 334-345. 该论文的作者提供了一个R包，其中包含本文中讨论的其他算法 我还提供了一个Jupyter笔记本，用于评估综合数据集上的算法。

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.