Description: The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab. Platform: |
Size: 83968 |
Author:bevin |
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Description: this r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators.-this is r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators. Platform: |
Size: 3072 |
Author:meysa |
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Description: 利用Metropolis-Hastings准则实现对高斯分布的采样，建议分布是高斯分布-The use of standards to achieve Metropolis-Hastings sampling of the Gaussian distribution, the proposed distribution is a Gaussian distribution Platform: |
Size: 2048 |
Author:yinbin |
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Description: LightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algorithm, and allows small cluster to tackle very large data and model sizes through model scheduling and data parallelism architecture. LightLDA is implemented with C++ for performance consideration. Platform: |
Size: 49152 |
Author:siegfried |
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Description: 本文件包含Metropolis算法对函数进行抽样;显示生成样本的相关图和直方图. 其中文件：metropolis_hastings.m该文件包含4个示例，用于通过Metropolis-Hastings算法对复杂函数进行抽样，显示生成样本的相关图和直方图。metropolis_hastings2.m
包含一个例子，用于通过Metropolis-Hastings算法对双变量高斯PDF进行采样，显示生成样本的相关图和直方图，以及其轮廓和边缘PDF的函数等。(This program develops a very basic example, for the sampling of functions by means of Metropolis algorithm; showing the correlograms and the histogram of the generated samples. metropolis_hastings.m. This file contains four examples, for the sampling of complex functions by means of Metropolis-Hastings algorithm, showing the correlograms and the histograms of the generated samples. In this case the proposals PDF its no longer symmetric. Additionally, the burn-in period, the lag period and the Geweke test have been implemented.It needs the "MH_routine.m" function.
metropolis_hastings2.m. This file contains one example, for the sampling of a bivariate Gaussian PDF by means of Metropolis-Hastings algorithm, showing the correlograms and the histograms of the generated samples, and the function with its contours and marginals PDF. Additionally, the burn-in period, the lag period and the Geweke test have been implemented.) Platform: |
Size: 11264 |
Author:3Radiant
|
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Description: 一种用于对各类概率密度函数进行样本采样的Metropolis-Hastings算法(a Metropolis-Hastings algorithm for sampling from various probability density functions) Platform: |
Size: 1024 |
Author:daoguangdong |
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Description: 吉布斯(Gibbs)抽样方法是 Markov Chain Monte Carlo(MCMC)方法的一种,也是应用最为广泛的一种(The simplest Gibbs sampling is a special case of Metropolis-Hastings algorithm, while the extension of Gibbs sampling can be regarded as a universal sampling system. This system takes a sample of each (or each) variable by rotation and combines a Metropolis-Hastings algorithm (or more complex algorithms, such as slice sampling, adaptive rejection sampling, and adaptive rejection Metropolis algorithm) to take a step or multistep sampling of a large number of variables.) Platform: |
Size: 13312 |
Author:Lynn12345 |
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Search - Metropolis-Hastings