文件名称:ReversibleJumpMCMCSimulatedAnneaing
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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
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下载文件列表
压缩包 : 27796713reversiblejumpmcmcsimulatedanneaing.rar 列表 Reversible Jump MCMC Simulated Annealing\report5.log Reversible Jump MCMC Simulated Annealing\report5.pdf Reversible Jump MCMC Simulated Annealing\report5.ps Reversible Jump MCMC Simulated Annealing\rjMCMCsa\gengamma.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjCubic.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjdemo1sa.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjGaussian.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjMultiquadric.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjsann.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\rjtpSpline.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarBirth.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarDeath.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarMerge.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarRW.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarSplit.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa\sarUpdate.m Reversible Jump MCMC Simulated Annealing\rjMCMCsa Reversible Jump MCMC Simulated Annealing