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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.
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Size: 348783 |
Author: 晨间 |
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Description: 一个可逆跳转蒙特卡罗采样（RJMCMC）算法详细程序，内附相关论文，对照论文看算法，便于理解。包含多种运动方式（增加，减少，分裂，合成，更新）
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Size: 656292 |
Author: 颜靖华 |
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Description: gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation.
It is a very good surce code for intelligent reasoning research-gibbs, beyesian network, intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research
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Size: 27648 |
Author: 程红 |
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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: 一个可逆跳转蒙特卡罗采样（RJMCMC）算法详细程序，内附相关论文，对照论文看算法，便于理解。包含多种运动方式（增加，减少，分裂，合成，更新）-A Reversible Jump Monte Carlo sampling (RJMCMC) algorithm detailed procedures, enclosing the relevant papers, watch the control thesis algorithm, easy to understand. Contains a wide range of movement (increase, decrease, fragmentation, synthesis, update)
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Size: 656384 |
Author: 颜靖华 |
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Description: 实现带有再采样步骤的rjmcmc算法，并用算例证明其性能。-To achieve with the re-sampling step rjmcmc algorithm, and use examples to prove its performance.
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Size: 16384 |
Author: yangmeng |
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Description: 非高斯信号信号处理，RJmcmc方法同时实现alpha稳定分布模型估计和参数估计的一篇重要文献。-nongaussian signal processing，RJMCM algorithm，alpha stable distribution
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Size: 492544 |
Author: szh |
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Description: 可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络-Reversible Jump MCMC Bayesian Model Selection
This demo demonstrates 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: 17408 |
Author: gaofei |
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Description: 可逆跳转马尔科夫链蒙特卡罗算法，可用于图像处理、音频处理等领域，其中有详细解释-rjmcmc simulation aims at approximate a noisy nolinear function
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Size: 37888 |
Author: Linda |
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Description: 一个可逆跳转蒙特卡罗采样（RJMCMC）算法详细程序，包含多种运动方式（增加，减少，分裂，合成，更新）,内附相关论文，对照论文看算法，便于理解。-A reversible jump Monte Carlo sampling (RJMCMC) algorithm detailed program, including a variety of sports mode (increase, decrease, split, synthesis, update), enclosing the relevant papers, control thesis algorithm, easy to understand.
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Size: 656384 |
Author: setb |
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Description: 对普通 RJMCMC算法提出了改进算法，并由MATLAB实现。-Ordinary RJMCMC algorithm proposed an improved algorithm, MATLAB realization by.
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Size: 3072 |
Author: 杨正茂 |
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Description: matlab解决MCMC问题的源代码 源于剑桥一篇report -use matlab solve MCMCproblems
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Size: 656384 |
Author: 郭长帅 |
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Description: Included in this distribution is matlab code to generate posterior samples for
linear Gaussian and binary matrix factorization (noisy-or) Indian Buffet
Process models. Three different posterior sampling algorithms are provided:
Gibbs, reversible jump Markov chain Monte Carlo (RJMCMC), and sequential
importance sampling (SIS). Only the Gibbs and SIS samplers are provided for
the linear Gaussian IBP models.-Included in this distribution is matlab code to generate posterior samples for
linear Gaussian and binary matrix factorization (noisy-or) Indian Buffet
Process models. Three different posterior sampling algorithms are provided:
Gibbs, reversible jump Markov chain Monte Carlo (RJMCMC), and sequential
importance sampling (SIS). Only the Gibbs and SIS samplers are provided for
the linear Gaussian IBP models.
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Size: 8368128 |
Author: 赵逸笙 |
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Description: Nando de Freitas' sequential Monte Carlo demos in Matlab. Reversible Jump MCMC Bayesian Model Selection.
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Size: 20480 |
Author: Comaero
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