The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.

这是一组用于进行贝叶斯线性回归的 MATLAB 函数。 派生也包括在内。

使用Gibbs采样对DINA模型进行参数估计，据论文实验结果可以看出，相对于MCMC方法，基于Gibbs采样的方法能获得更高得精度和速度。

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