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Description: The Swendsen-Wang Cuts algorithm is used to label atomic regions (superpixels) based on their intensity patterns using generative models in a Bayesian framework. The prior is based on areas of connected components, which provides a clean segmentation result. A performance comparison of the Swendsen-Wang Cuts algorithm with the Gibbs sampler shows that our algorithm is 400 times faster.
A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang for Image Analysis. J. Comp. Graph. Stat. 16, No 4, 2007 (pdf)
A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, PAMI, 27, August 2005 (pdf)
A. Barbu, S.C. Zhu. Graph Partition By Swendsen-Wang Cuts, ICCV 2003 (pdf)
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Author: bevin |
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Description: infinite HMM
Refer to Beal s Paper
Applying Gibbs Sampling in inference
the number of states of HMM is changeable in this algorithm, based on Dirichlet Proce-infinite HMMRefer to Beal s PaperApplying Gibbs Sampling in inferencethe number of states of HMM is changeable in this algorithm, based on Dirichlet Proce
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Size: 344064 |
Author: nielsting |
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Description: Bayes model averaging with selection of regressors - This program can be utilized for Bayesian Variable Selection using Gibbs Sampler-Bayes model averaging with selection of regressors- This program can be utilized for Bayesian Variable Selection using Gibbs Sampler
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Size: 9216 |
Author: Ruo Wang |
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Description: 这是一本讲述matlab中涉及到统计方面的知道书籍，特别是对采样，GIbbs采样，都有很好的示例。-This is the one involved in matlab about statistics to know the book, especially for sampling, GIbbs sample, there are good examples.
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Size: 8008704 |
Author: xiao |
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Description: this r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators.-this is r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators.
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Size: 3072 |
Author: meysa |
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Description: 分割是在MRI analysis.We的基本问题之一，同时考虑了多种MR图像分割，其中，例如，可能是一个系列的问题经过一段时间的扫描相同的组织（的2D/3D）图像，图像的数量，或不同的切片图像的对称部分。 MR图像的多是分割份额常见的结构信息，因此他们可以协助彼此分割的程序。我们提出了一个贝叶斯共同分割算法在共享的信息整个图像是通过利用马尔可夫随机场前，和吉布斯采样后采样是有效的聘用。由于我们的共同拉动分割算法考虑到所有的图像信息的同时，它提供比个人更准确和坚实的结果分割，如支持从模拟和实际结果的例子。-Segmentation is one of the basic problems in MRI analysis.We consider the problem of simultaneously segmenting multiple MR images, which, for example, could be a series
of (2D/3D) images of the same tissue scanned over time,different slices of a volume image, or images of symmetric parts. The multiple MR images to be segmented share
common structure information and hence they are able to assist each other in the segmentation procedure. We propose a Bayesian co-segmentation algorithm where the shared information
across images is utilized via a Markov random field prior, and a Gibbs sampler is employed for efficient posterior sampling. Because our co-segmentation algorithm pulls
all the image information into consideration simultaneously,it provides more accurate and robust results than the individual
segmentation, as supported by results from both simulated and real examples.
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Size: 812032 |
Author: 命运 |
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Description: MCMC（马尔科夫链蒙特卡洛）中对于高斯分布的Gibbs采样算法-MCMC Gibbs Sampler
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Size: 2048 |
Author: 汪鹏 |
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Description: 贝叶斯分析，比较复杂的自回归分析。VAR模型，注意比AR要先进的多！-Bayesian estimation, prediction and impulse response analysis in VAR models using the Gibbs sampler.
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Size: 9216 |
Author: xu |
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Description: EM算法的详细介绍，包括EM算法的方法论、原理以及应用等
-This new edition remains the only single source to offer a complete and unified treatment of the theory, methodology, and applications of the EM algorithm. The highly applied area of statistics here outlined involves applications in regression, medical imaging, finite mixture analysis, robust statistical modeling, survival analysis, and repeated-measures designs, among other areas. The text includes newly added and updated results on convergence, and new discussion of categorical data, numerical differentiation, and variants of the EM algorithm. It also explores the relationship between the EM algorithm and the Gibbs sampler and Markov Chain Monte Carlo methods.
With plentiful pedagogical elements-chapter introductions, author and subject indices, exercises, and computer-drawn graphics-this Second Edition of The EM Algorithm and Extensions will prove an essential companion for students and practitioners of advanced statistics
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Author: 天之翔 |
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Description: 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 properties are rarely examined, while
application-specific models and non-probabilistic learning
are gaining increased attention. In this paper we revisit
the generative aspects of MRFs, and analyze the quality of
common image priors in a fully application-neutral setting.
Enabled by a general class of MRFs with flexible potentials
and an efficient Gibbs sampler, we find that common models
do not capture the statistics of natural images well. We
show how to remedy this by exploiting the efficient sampler
for learning better generative MRFs based on flexible potentials.
We perform image restoration with these models
by computing the Bayesian minimum mean squared error
estimate (MMSE) using sampling. This addresses a number
of shortcomings that have limited generative MRFs so far,
and le
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Size: 1216512 |
Author: 孙文义 |
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Description: 此为一篇介绍mcmc方法的外文文献 非常适合刚刚接触mcmc方法的初学者-This paper introduces the readers of the Proceedings
to an important class of computer based simulation
techniques known as Markov chain Monte Carlo
(MCMC) methods. General properties characterizing
these methods will be discussed, but the main emphasis
will be placed on one MCMC method known as the
Gibbs sampler. The Gibbs sampler permits one to simulate
realizations from complicated stochastic models in
high dimensions by making use of the model’s associated
full conditional distributions, which will generally have
a much simpler and more manageable form. In its most
extreme version, the Gibbs sampler reduces the analysis
of a complicated multivariate stochastic model to the
consideration of that model’s associated univariate full
conditional distributions.
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Size: 464896 |
Author: long li |
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Description: 未知的稀疏序列convolved盲反褶积的一个未知的脉搏,一个强大的贝叶斯方法采用吉布斯采样器结合Bernoulli-Gaussian之前建模稀疏。-Unknown sparse sequence convolved blind deconvolution of an unknown pulse, a powerful Bayesian approach using Gibbs sampler combined modeling sparse before Bernoulli-Gaussian.
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Author: kobe |
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Description: Explaining the Gibbs Sampler
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Author: yyq |
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Description: lda gibbs sampler,which is implement in Java.
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Author: Mar |
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Description: 如何使它工作：
1。创建一个单独的目录，并将所有这些文件下载到相同的目录中
2。下载7个文件：
*demo:主文件demo:PMF和贝叶斯PMF
* PMF.m:训练的PMF模型
* bayespmf.m贝叶斯PMF模型实现吉布斯采样器。
* moviedata.mat样本数据包含三元组（user_id，movie_id，评分）
* makematrix.m：辅助功能转换成大型矩阵的三元组。
* PRED.m:辅助功能使得预测验证集。
三.在Matlab只需运行演示。此代码使用MATLAB统计工具箱从威沙特分布的样本。-How to make it work:
1. Create a separate directory and download all these files into the same directory
2. Download the following 7 files:
* demo.m Main file for training PMF and Bayesian PMF
* pmf.m Training PMF model
* bayespmf.m Bayesian PMF model that implements Gibbs sampler.
* moviedata.mat Sample data that contains triplets (user_id, movie_id, rating)
* makematrix.m Helper function that converts triplets into large matrix.
This file is used by bayespmf.m
* pred.m Helper function that makes predictions on the validation set.
* README.txt
3. Simply run demo.m in Matlab. It will fit PMF and then will run Bayesian PMF.
This code uses Matlab stats toolbox to sample Wishart distribution.
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Size: 4670464 |
Author: kobepudn |
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Description: 通过进行吉布斯抽样和PDE扩散来进行图像修复。原始图像具有（红色、绿色、蓝色）波段，通过在原始图像的红带上施加掩码图像来生成失真图像。(This is a task of image restoration and inpainting with two methods - **Gibbs sampler** and **PDE diffusion**. The original image has (Red, Green, Blue) bands, and the distorted image is created by imposing the mask image on the Red-band of the original image.)
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Size: 1658880 |
Author: markyT |
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