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Bayesian mixture of Gaussians. This set of files contains functions for performing inference and learning on a Bayesian Gaussian mixture model. Learning is carried out via the variational expectation maximization algorithm.
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Mixture of linear regressors. The routines contained in this file allow inference and learning of a mixture of linear-Gaussian regression models. Learning is performed by maximizing the data likelihood via the expectation maximization algorithm.
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Linear dynamical system. This set of functions performs inference and learning of a linear Kalman filter model. Inference is carried out via forward-backward smoothing, and learning is accomplished via the expectation maximization algorithm.
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This a reference implementation for the synthetic experiments on lower
linear envelope inference and learning described in
"Max-margin Learning for Lower Linear Envelope Potentials in Binary
Markov Random Fields", Stephen Gould, ICML 2011
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code producing figure 1.12 of the book "informatioin theory,inference,and learning algorithm"
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exercise 4.11 of the book "informatioin theory,inference,and learning algorithm"
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produce figure 4.9 of the book "informatioin theory,inference,and learning algorithm"
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exercise4.11 of the book "informatioin theory,inference,and learning algorithm"
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在matlab开发环境下 对贝叶斯网络结构进行学习 推理 计算分类，并且对它进行性能分析和比较-Matlab development environment for learning Bayesian network structure inference to calculate the classification, and its performance analysis and comparison
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本书从工程应用与实践的角度，对模糊推理与模糊控制系统作了深入浅出的介绍，并以多
个实例详细地介绍了模糊推理的学习及其在 matlab模糊逻辑工具箱中的实现，使得读者可以尽快地掌握模糊逻辑的内容与模糊控制的实现和使用。-Book from the point of view of engineering application and practice of fuzzy reasoning and fuzzy control system was introduced in simple te
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GPML Matlab Code
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexibl
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In mathematics, a relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification.The RVM has an identical functional form to the support vector machine, b
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source code of computer vision,models,learning and inference.
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