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rjMCMC.zip - Nando de Freitas' sequential Monte Carlo demos in Matlab. Reversible Jump MCMC Bayesian Model Selection.
rjMCMC1.rar - 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）
rjMCMCsa.zip - 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
rjMCMC.zip - 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
RJ-MCMC.rar - 可逆挑转马尔科夫链门特卡洛算法实现代码(在matlab下实现的)
Reversible_Jump_MCMC_Bayesian_Model_Selection.rar - 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.