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Description: 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.
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Size: 16422 |
Author: 徐剑 |
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Description: 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.
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Size: 220044 |
Author: 晨间 |
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Description: Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
algorithm to sample from the distribution P ~ EXP(-F), where F is the
first argument to HMC. The Markov chain starts at the point X, and
the function GRADF is the gradient of the `energy function F.
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Size: 3249 |
Author: 西晃云 |
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Description: 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.
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Size: 16384 |
Author: 徐剑 |
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Description: 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.
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Size: 220160 |
Author: 晨间 |
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Description: Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
algorithm to sample from the distribution P ~ EXP(-F), where F is the
first argument to HMC. The Markov chain starts at the point X, and
the function GRADF is the gradient of the `energy function F.
Platform: |
Size: 3072 |
Author: 西晃云 |
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Description: 巨正则系综蒙特卡罗算法的源程序；可以用来进行吸附等分子模拟；最大的好处在于可以插入或删除原子-Grand canonical ensemble Monte Carlo algorithm source can be used for adsorption, molecular simulation biggest advantage is that you can insert or delete atom
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Size: 102400 |
Author: lei ao |
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Description:
g(x)=f(m,n,L), 其中,m,n,L均服从正态分布,分布情况也在所给的图中.
使用matlab,用蒙特卡罗模拟法 对该函数进行模拟,得出g(x)大于0的概率. -g (x) = f (m, n, L), which, m, n, L are subject to normal distribution, the distribution is also given in Fig. using matlab, using Monte Carlo simulation method of the function simulation, draw g (x) the probability of greater than 0.
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Size: 1024 |
Author: 王路 |
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Description: Monte Carlo localization
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Size: 7168 |
Author: ovidel |
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Description: 无香粒子滤波的一个matlab例程，其中有ekf,ukf,pf,upf-In these demos, we demonstrate the use of the extended Kalman filter (EKF), unscented Kalman filter (UKF), standard particle filter (a.k.a. condensation, survival of the fittest, bootstrap filter, SIR, sequential Monte Carlo, etc.), particle filter with MCMC steps, particle filter with EKF proposal and unscented particle filter (particle filter with UKF proposal) on a simple state estimation problem and on a financial time series forecasting problem. The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar-xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this di
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Size: 38912 |
Author: gaofei |
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Description: This the MATLAB code that was used to produce the figures and tables in Section V of
F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference
in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on Image
Processing, 2006.
1
MATLAB has the capability of running functions written in C. The files which hold the source
for these functions are called MEX-Files. Some functions of our codes are written in C.
The purpose of this software is to implement the MCVEM algorithm, described in the paper
mentioned above, when applied to Image Segmentation. MCVEM consists in combining approximation
techniques - based on variational EM - and simulation techniques - based on MCMC
-.
This software is the first version that is made publicly available.
2 How to
2.1 Obtain the source code
Download it from
http://www.tsi.enst.fr/gfort/INRIA/MCVEM.html
After unpacking the archive, you should obtain
• two-This is the MATLAB code that was used to produce the figures and tables in Section V of
F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference
in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on Image
Processing, 2006.
1
MATLAB has the capability of running functions written in C. The files which hold the source
for these functions are called MEX-Files. Some functions of our codes are written in C.
The purpose of this software is to implement the MCVEM algorithm, described in the paper
mentioned above, when applied to Image Segmentation. MCVEM consists in combining approximation
techniques - based on variational EM - and simulation techniques - based on MCMC
-.
This software is the first version that is made publicly available.
2 How to
2.1 Obtain the source code
Download it from
http://www.tsi.enst.fr/gfort/INRIA/MCVEM.html
After unpacking the archive, you should obtain
• two
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Size: 692224 |
Author: jeevithajaikumar |
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Description: 通过Monte Carlo模拟法计算IEEE 79系统失负荷量，要求放在F盘下运行-Computing IEEE 79 system via Monte Carlo simulation method loss of load, run under the requirements placed on the F drive
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Size: 20480 |
Author: xieyimiao |
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Description: 这个函数用来通过蒙特卡罗求积分，
f 要求的函数；
fail1 积分下限
fail2 积分上限
a，b 积分区域内x的积分上下限
c,d 积分区域内y的最大和最小值,c,d不是必须的参数，但是有之计算速度更快
n 求解迭代次数- This function is used to find the integral Monte Carlo, function f requirements fail1 integral limit fail2 integral upper limit a, regional integration within b limit c on x points, d- the maximum and minimum value of the integration region y, c, d is not required parameters, but the calculation speed is faster n Iteration number
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Size: 1024 |
Author: Robert |
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Description: Monte Carlo simulation of time-resolved photon fluence rate
* in response to an impulse of energy at time zero delivered as an
* isotropic point source. Photon propagation is 3D in spherical
* coordinates. The instantaneous relative fluence rate,
* F(r,t)/Uo [cm^-2 s^-1] where F(r,t) = [J cm^-2 s^-1] and Uo = [J].
* For the calculation, the value of Uo is assumed to be unity [1 J].
* Propagation is in an infinite homogeneous medium
* with no boundaries. This program is a minimal Monte Carlo
* program.-Monte Carlo simulation of time-resolved photon fluence rate
* in response to an impulse of energy at time zero delivered as an
* isotropic point source. Photon propagation is 3D in spherical
* coordinates. The instantaneous relative fluence rate,
* F(r,t)/Uo [cm^-2 s^-1] where F(r,t) = [J cm^-2 s^-1] and Uo = [J].
* For the calculation, the value of Uo is assumed to be unity [1 J].
* Propagation is in an infinite homogeneous medium
* with no boundaries. This program is a minimal Monte Carlo
* program.
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Size: 5120 |
Author: Hossein |
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Description: MCMC方法主要是为了解决有些baysian推断中参数期望E(f(v)|D)不能直接计算得到的问题的。
其中v是要估计的参数，D是数据观察值-The concept consists of two parts:markov chain and monte carlo integration。
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Size: 4096 |
Author: tzx |
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