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【AI-NN-PR】 automix DL : 0: he AutoMix package is a C program for Unix-like systems, implementing the automatic reversible jump MCMC sampler of the same name described in Chapters 4, 5, and 6 of David Hastie s Ph.D. thesis-he AutoMix package is a C program for Unix-l ike systems, implementing the automatic reversible jump MC MC sampler of the same name described in Chapter s 4, 5, and 6 of David Hastie's Ph.D. thesis
Update : 2024-05-06 Size : 93184 Publisher : sjtuzyk
【matlab】 upf_ekf_ukf_epf_demos DL : 0: 关于pf，ekf，ukf，upf，epf，并加上mcmc算法-On pf, ekf, ukf, upf, epf, and together with the MCMC algorithm
Update : 2024-05-06 Size : 63488 Publisher : wang meng
【AI-NN-PR】 ssmcmcmatlab DL : 0: semi-supervised MCMC classification
Update : 2024-05-06 Size : 20480 Publisher : 刘国亮
【Algorithm】 A_MCMC_approach_for_Bayesian_super-resolution_imag DL : 0: 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-MCMC methods for super-resolution paper, this thesis is based on Bayesian statistical papers, is another very effective method sr
Update : 2024-05-06 Size : 110592 Publisher : gba
【AI-NN-PR】 rjMCMCsa DL : 0: 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. -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.
Update : 2024-05-06 Size : 16384 Publisher : 徐剑
【Algorithm】 On-Line_MCMC_Bayesian_Model_Selection DL : 0: 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.-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.
Update : 2024-05-06 Size : 220160 Publisher : 晨间
【Algorithm】 Reversible_Jump_MCMC_Bayesian_Model_Selection DL : 0: 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.
Update : 2024-05-06 Size : 348160 Publisher : 晨间
【matlab】 MCMC DL : 0: 这是马尔可夫-蒙特卡罗算法的MATLAB源程序.-This is the Markov- Monte Carlo algorithm for MATLAB source code.
Update : 2024-05-06 Size : 136192 Publisher : liufanmao
【AI-NN-PR】 msvar DL : 1: MCMC（马尔可夫－盟特卡罗方法）实现的程序-MCMC (Markov- UNITA Monte Carlo method) procedures realize
Update : 2024-05-06 Size : 32768 Publisher : chenzhuo
【Mathimatics-Numerical algorithms】 27796735particlefilter-mcmc DL : 0: 大家可以参考的例子滤波源码，程序非常好用，能里脊肉粒子滤波的基础-Everyone can refer to examples of source filtering, the program is very easy to use, can fillet the basis of particle filter
Update : 2024-05-06 Size : 15360 Publisher : 周迎
【Documents】 mcmc1 DL : 1: 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。-This is the Los Alamos National Laboratory on mcmc (Markov chain Monte Carlo method) is simple tutorial suitable for this area has just come into contact with friends there will be some help.
Update : 2024-05-06 Size : 287744 Publisher : pengs
【Documents】 mcmc3 DL : 2: 这是Calgary大学的关于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。-This is the University of Calgary on the mcmc (Markov chain Monte Carlo method) is simple tutorial suitable for this area has just come into contact with friends there will be some help.
Update : 2024-05-06 Size : 464896 Publisher : pengs
【matlab】 87361032particle-filter-mcmc DL : 0: 针对无线传感器网络的节点的追踪算法matlab仿真。-For wireless sensor network nodes tracking algorithm matlab simulation.
Update : 2024-05-06 Size : 14336 Publisher : 王东
【Compress-Decompress algrithms】 mcmc DL : 0: 马尔科夫链蒙特卡洛模拟的matlab源代码-Markov chain Monte Carlo simulation of the matlab source code
Update : 2024-05-06 Size : 16384 Publisher : 左秀霞
【AI-NN-PR】 MCMCpack DL : 0: 使用R语言的马尔科夫链蒙特卡洛模拟（MCMC）源代码程序。-R languages using Markov chain Monte Carlo simulation (MCMC) procedures for source code.
Update : 2024-05-06 Size : 447488 Publisher : 左秀霞
【Other】 mcmc DL : 0: Tutorial Lectures on -Tutorial Lectures on
Update : 2024-05-06 Size : 119808 Publisher : szh
【Special Effects】 mcmcstuff DL : 0: 本源码是基于Markov chain Monte Carlo (MCMC)的Bayesian inference工具包,其中包括MCMC采样，基于MCMC的高斯分类，同时描述了采样的一些方法。其中还有使用文档-toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods
Update : 2024-05-06 Size : 11885568 Publisher : 吴晓明
【Other】 mcmc DL : 0: 蒙特卡洛模拟算法，和大家一起交流，共同进步-Monte Carlo simulation algorithm, and the community together to exchange and common progress
Update : 2024-05-06 Size : 16384 Publisher : yinshaohua
【Special Effects】 MCMC DL : 0: 有关于粒子滤波方面的一个具体实现程序代码-particle filtering
Update : 2024-05-06 Size : 14336 Publisher : 赵小岷
【AI-NN-PR】 SimulationandMonteCarlo DL : 0: the book <<Simulation and Monte Carlo With applications in finance and MCMC >> about MONte carlo method applying to finance problem and markov chain and markov decision process.
Update : 2024-05-06 Size : 3390464 Publisher : 胡桃

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