作者: John Kruschke 出版社: Academic Press 副标题: A Tutorial with R, JAGS, and Stan 出版年: 2014-11-17 页数: 776 定价: USD 89.95 装帧: Hardcover ISBN: 9780124058880

新的回归向量机，用于回归拟合，相关论文可以参考sparse bayesian learning and relevance vector machine

英文原版pdf资料《Statistical Rethinking A Bayesian Course with Examples in R and Stan》

The object of this book is to explore the use and relevance of Bayes' theorem to problems such. as arise in scientific investigation in which inferences must be made concerning parameter values about which little is known a priori.

Probreg is a library that implements point cloud registration algorithms with probablistic model. The point set registration algorithms using stochastic model are more robust than ICP(Iterative Closest Point). This package implements several algorithms using stochastic models and provides a simple interface with . Core features Open3D interface Rigid and non-rigid transformation Algorithms Maximum

由于模型固有的不确定性，学习从少量数据集推断贝叶斯后验是实现稳健元学习的重要一步。在本文中，我们提出了一种新的贝叶斯模型不可知的元学习方法。该方法结合了有效的基于梯度的元学习和非参数变分推理。与以往的方法不同的是，在快速适应过程中，该方法能够学习复杂的不确定性结构，而不是简单的高斯近似;在元更新过程中，采用了新的贝叶斯机制，防止了元级过拟合。它仍然是一种基于梯度的方法，也是第一个适用于包括强化学习在内的各种任务的不依赖贝叶斯模型的元学习方法。实验结果表明，该方法在正弦回归、图像分类、主动学习和强化学习等方面具有较好的准确性和鲁棒性。

用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets

非常棒的粒子滤波综述，详细介绍了卡尔曼滤波到粒子滤波的演变历程。

em算法matlab代码带变分贝叶斯层次EM的隐马尔可夫模型聚类 隐马尔可夫模型（HMM）是一种广泛使用的用于表示时间序列数据的生成模型，而聚类HMM吸引了机器学习研究人员的浓厚兴趣。 但是，仍然难以确定集群中心的集群数（K）和隐藏状态数（S）。 在本文中，我们提出了一种新颖的基于HMM的聚类算法，即变分贝叶斯分层EM算法，该算法通过其密度和先验性对HMM进行聚类，并同时学习紧凑地表示每个聚类结构的新型HMM聚类中心的后验。 数字K和S以两种方式自动确定。 首先，我们在该对（K，S）上放置一个先验值，然后近似其后验概率，从中选择具有最大后验概率的值。 其次，当没有数据样本分配给它们时，一些簇和状态被隐式删除，从而导致模型复杂度的自动选择。 代码实施 该工具箱包含VBHEM-H3M的主要功能，并且基于Matlab。 其中包括 setup.m：设置工具箱的路径。 src： vbhem：VBHEM算法。 hmm：用于学习HMM的VBEM。 compare_mtds：本文使用的比较方法，CCFD，VHEM，DIC和PPK。 plots：用于绘制结果。 util：其他代码。 演示：使用VBHEM

The object of this book is to explore the use and relevance of Bayes' theorem to problems such. as arise in scientific investigation in which inferences must be made concerning parameter values about which little is known a priori.