This demo nstrates the use of the reversible jump mcmc simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
标签： This reversible annealing the
上传时间： 2015-07-19
上传用户：ma1301115706
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.
标签： Selection Bayesian CARLO Model
上传时间： 2013-12-22
上传用户：ynwbosss
semi-supervised mcmc classification
标签： semi-supervised classification mcmc
上传时间： 2016-01-05
上传用户：顶得柱
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
上传时间： 2016-02-04
上传用户：pinksun9
mcmc 马尔可夫链 蒙特卡罗算法 具体参数 请用help命令
上传时间： 2013-12-25
上传用户：zsjzc
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.
标签： demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：lindor
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.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
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.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
mcmc（马尔可夫－盟特卡罗方法）实现的程序
上传时间： 2013-12-17
上传用户：gououo
这是Los Alamos国家实验室的关于mcmc（马尔科夫链蒙特卡洛法）的简明教程，适合于刚刚接触到这一领域的朋友会有一些帮助。
上传时间： 2014-01-22
上传用户：hongmo
