We consider two particular Bayesian network structures, the so-called nau00efve Bayes and TAN, which have been successfully used as regression models when learning from complete data. We propose an iterative procedure for inducing the models, based on a variation of the data augmentation method ...
Factor Graph 是概率图的一种,概率图有很多种,最常见的就是Bayesian Network (贝叶斯网络)和Markov Random Fields(马尔可夫随机场)。在概率图中,求某个变量的边缘分布是常见的问题。这问题有很多求解方法,其中之一就是可以把Bayesian Network和Markov Random Fields 转换成Facor Graph,然后用sum-product算法求解。 Bayes...
一、贝叶斯网络(Bayesian Network) 1.对概率图模型的理解 概率图模型是用图来表示变量概率依赖关系的理论,结合概率论与图论的知识,利用图来表示与模型有关的变量的联合概率分布。 对于一个实际问题,我们希望能够挖掘隐含在数据中的知识。概率图模型构建了这样一幅图,用观测结点表示观测到的数据,用隐含结点表示潜在的...
The relaxation of stationary hypothesis for DBN leads to a highly flexible model. This might lead to over-fitting or inflated inference uncertainty, especially when the subsequent transition times are close together and the network structures must be inferred from short time series segments. To addres...
This example shows how to train a Bayesian neural network (BNN) for image regression using Bayes by backpropagation[1]. You can use a BNN to predict the rotation of handwritten digits and model the uncertainty of those predictions. A Bayesian neural network (BNN) is a type of deep learning...
We apply our method to a brain connectome dataset that contains information on brain networks along with a measure of creativity for multiple individuals. Here, interest lies in building a regression model of the creativity measure on the network predictor to identify important regions and connections...
Using several breast cancer-specific datasets, we demonstrated the effectiveness of Bayesian network modeling in biological meaningful signal discovery, in comparison with methods of linear regression. Potentially, Bayesian inference can be used to infer dynamic GRN during cell differentiation using new ...
In addition, we relateBayesian-network methods for learning to techniques for supervised andunsupervised learning. We illustrate the graphical-modeling approachusing a real-world case study. 展开 关键词: Bayesian networks Bayesian statistics learning missing data classification regression clustering causal ...
The current method achieved a correlation coefficient R-2 higher than 99% in both porosity and permeability regression in the blind test sample, i.e., using a different set of data that was not applied in the training procedure. 展开 关键词: Permeability Convolutional network Deep learning ...
A Bayesian neural network predicts a variance along with the average mean, considering the many environmental changes that may happen in the distant future. Two regression models compare the results of a standard neural network and a Bayesian neural network predicting CO2 concentration tren...