Stephenson (2009), Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems, Mar. Pet. Geol., 26(4), 525-535.Gallagher K., Charvin K., Nielsen S., Sambridge M. and Stephenson J. (2009) Markov chain ...
蒙特卡罗采样 (Monte Carlo Sampling) 对于⼤多数实际应⽤中的概率模型来说,精确推断是不可⾏的,因此不得不借助于某种形式的近似。上一章讨论了变分推断的近似⽅法,本章就考虑基于数值采样的近似推断⽅法,也被称为蒙特卡罗采样方法 (Monte Carlo Sampling Method)。 在贝叶斯神经网络一节中,我们提到过针对连...
2.2. 基于Markov Chain采样 3. Markov Chain Monte Carlo (MCMC) and Metropolis-Hastings (MH) 3.1. Detailed Balance 3.2. MCMC 3.3. Metropolis-Hastings Sampling 4. Gibbs Sampling 4.1. 重新寻找细致平稳条件 4.2. 多维Gibbs采样 References 0. Main Takeaway Sec 1. 介绍了蒙特卡洛采样 (MC) 和拒绝-接受...
一般而言,均匀分布Uniform(0,1)的样本容易生成,而常见的概率分布(连续或离散)都可以基于均匀分布的样本生成,例如正态分布可以通过Box-Muller变换得到. 但是像p(x,y,z)这样甚至更高维度分布的样本很难生成,而MCMC(Markov Chain Monte Carlo)和Gibbs Sampling算法就是解决这个问题的.让我们从马尔科夫链(Markov Chain...
MCMC（Markov Chain Monte Carlo）是一种利用马尔可夫链的采样技术。它通过构建满足详和平衡条件的转移矩阵来模拟目标分布的样本。在MCMC中，Metropolis-Hastings算法是一种改进的MCMC方法，通过调整接受率来提高采样效率。Gibbs Sampling是MCMC的一种特殊形式，适用于条件独立的随机变量。通过交替更新每个变量的...
Markov Chain Monte Carlo 和 Gibbs Sampling算法 Welcome To My Blog 一.蒙特卡洛模拟 蒙特卡洛模拟(Monte Carlo Simulation)是随机模拟的别名,关于随机模拟的一个重要的问题就是:给定一个概率分布p(x),如何生成它的样本? 一般而言,均匀分布Uniform(0,1)的样本容易生成,而常见的概率分布(连续或离散)都可以基于均匀...
The Markov chain Monte Carlo (MCMC) approach is used to infer the statistical parameter of naturally occurring gamma-induced radionuclides such as Th, K and Ra. We used a bootstrapping method to obtain an accurate sub-sample and then exclude all potential outliers which are out of the ...
In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory of Markov chains, concentrating particularly on the conditions required for the existence of a stationary...
Here, we report a machine learning scheme that exploits memristor variability to implement Markov chain Monte Carlo sampling in a fabricated array of 16,384 devices configured as a Bayesian machine learning model. We apply the approach experimentally to carry out malignant tissue recognition and heart...
MCMC全称是Markov Chain & Monte Carlo。 在概率图的框架中属于近似推断中的不确定性推断,与之相对的有近似推断中的变分推断(variational Inference)。 MCMC本质是基于“采样”的“随机”“近似”。有三个关键词。 ①采样是说MCMC本质就是一种引入Markov Chain模型实现采样任务的一种方法,本质是一种采样方法(Method...