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Description: 该程序为基于粒子滤波的一种新算法,综合MCMC Bayesian Model Selection即MONTE CARLO马尔克夫链的算法,用来实现目标跟踪,多目标跟踪,及视频目标跟踪及定位等,解决非线性问题的能力比卡尔曼滤波,EKF,UKF好多了,是我珍藏的好东西,现拿出来与大家共享,舍不得孩子套不着狼,希望大家相互支持,共同促进.-the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning. solve nonlinear problems of capacity than Kalman filtering, EKF, UKF much better, and I treasure the good stuff, now up with the share could not bear children, she sets the wolf, we hope that support each other and work together to promote.
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Size: 14805 |
Author: zhang |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type \"tar -xf version2.tar\" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type \"smcdemo1\". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16422 |
Author: 徐剑 |
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Description: 该程序为基于粒子滤波的一种新算法,综合MCMC Bayesian Model Selection即MONTE CARLO马尔克夫链的算法,用来实现目标跟踪,多目标跟踪,及视频目标跟踪及定位等,解决非线性问题的能力比卡尔曼滤波,EKF,UKF好多了,是我珍藏的好东西,现拿出来与大家共享,舍不得孩子套不着狼,希望大家相互支持,共同促进.-the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning. solve nonlinear problems of capacity than Kalman filtering, EKF, UKF much better, and I treasure the good stuff, now up with the share could not bear children, she sets the wolf, we hope that support each other and work together to promote.
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Size: 14336 |
Author: zhang |
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Description: MCMC方法的超分辨paper，此论文是已贝叶斯统计论文为基础，是另一种很有效的sr方法-MCMC methods for super-resolution paper, this thesis is based on Bayesian statistical papers, is another very effective method sr
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Size: 110592 |
Author: gba |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16384 |
Author: 徐剑 |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 220160 |
Author: 晨间 |
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Description: This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 348160 |
Author: 晨间 |
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Description: 本源码是基于Markov chain Monte Carlo (MCMC)的Bayesian inference工具包,其中包括MCMC采样，基于MCMC的高斯分类，同时描述了采样的一些方法。其中还有使用文档-toolbox is a collection of Matlab functions for Bayesian inference
with Markov chain Monte Carlo (MCMC) methods
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Size: 11885568 |
Author: 吴晓明 |
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Description: 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 standard models (normal, binomial, Poisson) and then get acquainted with computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications. Advanced topics include model comparison and decision theory.
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Author: terminator |
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Description: 这是国外研究Gibbs采样和Bayesian推理的研究人员写的工具包软件，最新版本为V1.4.3。很适合研究机器学习及其贝叶斯推理的科研人员使用。-The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project began in 1989 in the MRC Biostatistics Unit and led initially to the `Classic BUGS program, and then onto the WinBUGS software developed jointly with the Imperial College School of Medicine at St Mary s, London. Development now also includes the OpenBUGS project in the University of Helsinki, Finland. There are now a number of versions of BUGS, which can be confusing.
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Author: 王磊 |
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Description: MCMC方法是一种重要的模拟计算方法，马尔可夫链蒙特卡尔理论(Markov chain Monte Carlo：MCMC)的研究对建立可实际应用的统计模型开辟了广阔的前景。90年代以来，很多应用问题都存在着分析对象比较复杂与正确识别模型结构的困难。现在根据MCMC理论，通过使用专用统计软件进行MCMC模拟，可解决许多复杂性问题。此外，得益于MCMC理论的运用，使得贝叶斯(Bayes)统计得到了再度复兴，以往被认为不可能实施计算的统计方法变得是很轻而易举了-MCMC method is an important simulation methods, Markov chain Mengtekaer theory (Markov chain Monte Carlo: MCMC) research on the establishment of the practical application of the statistical model can be opened up broad prospects. Since the 90' s, there are a lot of application problems are more complex object model structure with the correct identification difficult. Now under the MCMC theory, through the use of special statistical software MCMC simulation can solve many complex problems. In addition, thanks to the use of MCMC theory makes Bayesian (Bayes) statistics have been re-revival in the past that were considered impossible calculation of statistical methods is very easy to become a
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Size: 2323456 |
Author: 曹哥 |
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Description: the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning-the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning
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Size: 2048 |
Author: FOUFOU2 |
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Description: 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 computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications.-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 computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications.
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Size: 7168 |
Author: FOUFOU2 |
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Description: the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking -the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking
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Size: 1024 |
Author: PG_usthb |
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Description: 本程序是基于马尔可夫链蒙特卡尔理论的贝叶斯工具。版本为MCMC Methods for MLP and GP and Stuff (for Matlab) V2.1 -A collection of matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. The purpose of this toolbox was to port some of the features in fbm to matlab for easier development for matlab users.
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Size: 11837440 |
Author: hu |
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Description: 该程序实现了马尔科夫蒙特卡洛模拟方法，是贝叶斯估计不可缺少的程序-this algorithm realise Markov-monte carol algorithm and is indispensible for Bayesian estimation.
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Size: 15360 |
Author: 黄安强 |
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Description: 论文：基于MCMC的贝叶斯生存分析理论及其在可靠性评估中的应用。对于贝叶斯中的mcmc算法介绍较详细-Thesis: Based on MCMC Bayesian theory of survival analysis and reliability assessment. Mcmc in Bayesian algorithm introduced in more detail
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Author: 鱼淼 |
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Description: 一篇硕士学位论文，对贝叶斯中的mcmc方法论述得比较清楚-A master' s degree thesis, discusses the mcmc the Bayesian method was clearly
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Size: 772096 |
Author: 鱼淼 |
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Description: 该程序为基于粒子滤波的一种新算法,综合MCMC Bayesian Model Selection即MONTE CARLO马尔克夫链的算法,用来实现目标跟踪,多目标跟踪,及视频目标跟踪及定位等,解决非线性问题的能力比卡尔曼滤波,EKF,UKF好多了,是我珍藏的好东西,现拿出来与大家共享,舍不得孩子套不着狼,希望大家相互支持,共同促进--the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning. solve nonlinear problems of capacity than Kalman filtering, EKF, UKF much better, and I treasure the good stuff, now up with the share could not bear children, she sets the wolf, we hope that support each other and work together to promote.
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Size: 21504 |
Author: 阿萨德 |
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Description: 马尔科夫链蒙特卡洛算法简单实例,模式识别，参数识别(mcmc,bayesian,Based on markov chain monte carlo method is implemented in the matlab program)
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Author: 马尔科夫贝叶斯
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