搜索资源列表
Bayesianmethods
- 本压缩文件详细介绍了Robert Piche博士关于贝叶斯算法的理论和他的笔记，其中文档中还包含源码程序，另附两个m文件源程序，是一个非常实用的学习及参考资料-In this course we present the basic principles of Bayesian statistics (an alternative to "orthodox" statistics). We start by learning how to estimate parameters for stand
proj03-02
- 考虑两个参数的一维三角概率模型,用贝叶斯方法对其进行估计，了解贝叶斯估计方法.-Consider the two parameters of one-dimensional triangular probability model, using Bayesian methods to estimate their understand Bayesian methods.
nouv1
- In this course we present the basic principles of Bayesian statistics (an alternative to "orthodox" statistics). We start by learning how to estimate parameters for standard models (normal, binomial, Poisson) and then get acquainted with computationa
Bayesguji
- 用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-With the supervision of the Bayesian estimation method to estimate the parameters of the conditional probability density of u
bayesian-matting
- 基于贝叶斯的抠像算法，通过一个最大似然的标准去估计透明度-A Bayesian Approach to Digital Matting，uses a maximum-likelihood criterion to estimate the optimal opacity
Minimum-Bayes-classifier-error-rate
- 这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度
Minimum-Risk-Bayes-classifier
- 这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从
leaves-bass-algorithm
- 这是一个贝叶斯独立分量分析(ICA)算法的线性瞬时混合高斯噪声模型和添加剂。解决问题的是ML-II推论,即资源的整合在发现源后和噪声协方差矩阵和混合了最大化的边际似然。充分统计量的估计平均场或变分理论和线性响应修正或通过自适应平均场理论水龙头。平均场方程,解决了信仰传播法的或连续的迭代。-This is a bayesian independent component analysis (ICA) algorithm of instantaneous linear mixed gaussian
VistaRestoreTools1.0
- denoise In BayesShrink[5] we determine the threshold for each subband assuming a Generalized Gaussian Distribution(GGD) . The GGD is given by GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6) ¡ 1 <
mmse_mrf_demo-1.1
- 图像去噪-A Generative Perspective on MRFs in Low-Level Vision-A Generative Perspective on MRFs in Low-Level Vision Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative
deblurring_demo-1.0
- Bayesian Deblurring with Integrated Noise Estimation-Bayesian Deblurring with Integrated Noise Estimation Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequenc
GGDimagedenoising
- 我按这篇文章做的，广义高斯分布及其在图像去噪中的应用，没有完全做出来，谁做出来了，分享一下- The statistics of imagewavelet coefficients is non2Gaussian and can be described by generalized Gaussian distribution (GGD). The paper investigates the issues of GGD statisticalmodel for wavelet coeffi
train_mxlhb_06
- 利用贝叶斯原理估计混合logit模型的参数-Using bayesian principle to estimate the parameters of mixed logit model
MAP
- In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is a mode of the posterior distribution.
bieys1114
- 用监督参数估计中的贝叶斯方法估计条件概率密度的参数(Bayesian parameter estimation using supervised parameter estimation to estimate the conditional probability density)