Description: 基于有限高斯混合模型的EM算法的源程序代码，里面有实验报告和运行结果。
-based on finite Gaussian mixture model of the EM algorithm source code, which has run reports and experimental results. Platform: |
Size: 52224 |
Author:王 |
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Description: 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. Platform: |
Size: 136192 |
Author:liufanmao |
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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) Platform: |
Size: 656384 |
Author:颜靖华 |
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Description: PF,EPF,UPF的对比仿真代码，可以很直观的看出各种算法之间的差别及各自的优缺点。可直接运行。-PF, EPF, UPF contrast simulation code, it is intuitive to see the differences between the various algorithms and their respective advantages and disadvantages. Can be directly run. Platform: |
Size: 16384 |
Author:李光 |
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Description: MCMC toolbox for Matlab
From this page you can download a set of Matlab function for some statistical MCMC analyses of mathematical models. This code might be useful to you if you are already familiar with Matlab and want to do MCMC analysis using it. Platform: |
Size: 72704 |
Author:hossein |
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Description: This the Matlab code that performs Reverisble jump MCMC by a faculty at Ohio State Univ.-The matlab code perfroms reversible jump MCMC for gene study coded by a faculty at Ohio State Univ. Platform: |
Size: 51200 |
Author:william_wu |
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Description: 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 Markov chain transfer gate pick Teka Luo algorithm code (in matlab under implementation) Platform: |
Size: 39936 |
Author:linda |
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Description: MCMC代码，非常适合初学者进行试验，加深理解，马尔可夫链蒙特卡罗-MCMC code, ideal for beginners to experiment, to deepen understanding, Markov chain Monte Carlo Platform: |
Size: 76800 |
Author:tianw |
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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 Platform: |
Size: 692224 |
Author:jeevithajaikumar |
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Description: 由matlab实现的从高斯分布数据中进行Gibbs采样的示例程序，代码中我已加注释，比较好理解，对于学习MCMC的同学比较有帮助。当初我理解GIbbs采样非常痛苦，希望这份代码对与我有相同经历的同学帮上忙-Realized by MATLAB the Gauss distribution data of Gibbs sampling sample program, the code I have to add a comment, is better understood, more helpful for students learning MCMC. I understand GIbbs sampling is very painful, hope this code on and I have the same experience of the students help
Platform: |
Size: 3072 |
Author:Arogon |
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