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  • 随机模拟
  • 参考佳文

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  1. MATH-碎碎念

sampling

PreviousLearning TheoryNextEntropy

Last updated 5 years ago

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随机模拟

统计模拟中有一个重要的问题就是给定一个概率分布p(x)p(x)p(x),我们如何在计算机中生成它的样本。

线性同余发生器 Xn=(aXn−1+b) mod mX_n =(aX_{n-1} + b) \bmod mXn​=(aXn−1​+b)modm 一般m为2的幂,方便计算机运算。发生器种子就是X0X_0X0​ 扩充,二次同余发生器 Xn=(aXn−12+bXn−1+c) mod mXn =(aX_{n-1}^2 + bX_{n-1}+c) \bmod mXn=(aXn−12​+bXn−1​+c)modm

主要点: 马尔科夫过程的平稳状态: lim⁡n→∞Pijn=π(j)\lim_{n\rightarrow\infty}P_{ij}^n = \pi(j)limn→∞​Pijn​=π(j) ,及细致平稳状态: π(i)Pij=π(j)Pjifor alli,j\pi(i)P_{ij} = \pi(j)P_{ji} \quad\quad \text{for all} \quad i,jπ(i)Pij​=π(j)Pji​for alli,j

参考佳文

随机采样方法整理与讲解(MCMC、Gibbs Sampling等)
再谈MCMC方法
LDA-math-MCMC 和 Gibbs Sampling
gibbs