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
標簽： This reversible annealing the
上傳時間： 2015-07-19
上傳用戶：ma1301115706
該程序為基于粒子濾波的一種新算法,綜合mcmc Bayesian Model Selection即MONTE CARLO馬爾克夫鏈的算法,用來實現目標跟蹤,多目標跟蹤,及視頻目標跟蹤及定位等,解決非線性問題的能力比卡爾曼濾波,EKF,UKF好多了,是我珍藏的好東西,現拿出來與大家共享,舍不得孩子套不著狼,希望大家相互支持,共同促進.
標簽： Selection Bayesian CARLO Model
上傳時間： 2013-12-22
上傳用戶：ynwbosss
semi-supervised mcmc classification
標簽： semi-supervised classification mcmc
上傳時間： 2016-01-05
上傳用戶：頂得柱
mcmc方法的超分辨paper，此論文是已貝葉斯統計論文為基礎，是另一種很有效的sr方法
上傳時間： 2016-02-04
上傳用戶：pinksun9
mcmc 馬爾可夫鏈 蒙特卡羅算法 具體參數 請用help命令
上傳時間： 2013-12-25
上傳用戶：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.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：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.
標簽： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：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.
標簽： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
mcmc（馬爾可夫－盟特卡羅方法）實現的程序
上傳時間： 2013-12-17
上傳用戶：gououo
這是Los Alamos國家實驗室的關于mcmc（馬爾科夫鏈蒙特卡洛法）的簡明教程，適合于剛剛接觸到這一領域的朋友會有一些幫助。
上傳時間： 2014-01-22
上傳用戶：hongmo
