搜索资源列表
vbssm
- 基于变分贝叶斯方法的状态空间模型学习程序-Based on the Variational Bayesian methods of state space model the learning process
vbhmm.tar
- HMM模型的变分贝叶斯方法Matlab程序。-The Matlab program of variational Bayesian for HMM model
VBLS
- 变分贝叶斯最小二乘法的matlab程序。-Variational Bayesian least-squares method matlab program
vbICA1_0.tar
- 该程序为变分贝叶斯独立分量分析算法，可以在强噪声环境下实现混合信号的盲分离，而且效果很好。-This code is the vbICA algirithm, which can separate the mixed signals in strong noisy environment, and the result better than other algorithms.
vbICA_1.0
- 基于变分贝叶斯的独立成分分析 模型包括一般的高斯混合模型以及高斯混合模型与隐马尔科夫结合在一起的-Based on variational Bayesian independent component analysis model consists of a general Gaussian mixture Gaussian mixture model and hidden Markov models and combined
vbICA_1.0
- 变分贝叶斯matlab程序包，三个demo:vbica1,vbica2,vbmoica-variational bayasian leaves matlab, bag, three demo: vbica1, vbica2, vbmoica
vbhmm
- Matthew J.Beal的变分贝叶斯理论在隐马尔可夫模型中的应用程序。多加了个转换程序。-VBEM of Matthew J.Beal to HMM.
paper3
- 基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法.pdf-Blurred image of the blind restoration algorithm based on variational Bayesian estimation of camera jitter. Pdf
variational-Bayesian
- 基于变分贝叶斯里理论的图像超分辨率重建技术，对于理解超分辨率技术有一定帮助。-In theory based on variational Bayesian image super-resolution reconstruction technique for understanding the super-resolution technology has certainly helped.
AnatLevin
- 本算法主要采用变分贝叶斯求解图像忙恢复问题。-This package contains implementations of the MAP_k blind deconvolution algorithms described in the paper "Efficient Marginal Likelihood Optimization in Blind Deconvolution" Levin, Weiss,
VBEMGMM
- 变分贝叶斯高斯过程混合模型源码，主要基于pattern recognition and machine learning 这本书。-22 Oct 2008 gmmVBEM.m is the main file for VBEM Main file needs MyEllipse.m for plotting Netlab gmmem for initialization Following ex
VBSSM
- 线性动态系统，基于变分贝叶斯的线性动态系统(linear dynamic system)
VB_Matlab_Tracking.zip
- 利用变分贝叶斯算法进行目标跟踪，可以应对噪声统计特性未知的情况。(This matlab code uses VB for target tracking.)
1
- 一篇关于变分贝叶斯解决噪声参数未知的论文代码，噪声分布使用了逆威沙特分布构建(A paper code about solving the unknown noise parameters with variable decibel Bayes. The noise distribution is constructed with inverse wissaud distribution)