论文标题
部分可观测时空混沌系统的无模型预测
Evaluating Posterior Distributions by Selectively Breeding Prior Samples
论文作者
论文摘要
使用马尔可夫链蒙特卡洛（Monte Carlo）从后验分布中进行采样是关键的创新，这使贝叶斯数据分析实用。然而，众所周知，MCMC很难调整，难以诊断，并且很难并行化。该教学说明探索了通用{\ em non} -markov-markov-carlo Carlo方案的变体，用于从后分布中抽样。基本思想是从先前的分布中绘制参数值，评估每个抽奖的可能性，然后复制与其可能性成正比的次数。复制后的分布是向后近似的近似值，随着初始样品的数量变为无穷大；近似的收敛很容易分析，并且在Glivenko-Cantelli类别上是均匀的。虽然不是完全实用的{\ em}，但这些方案是直接实现（几行），易于并行化的，并且不需要拒绝，刻录，收敛性诊断或对任何控制设置进行调整。我提到了先前的艺术，该艺术涉及一些实际障碍，以一定的计算和分析简单性。
Using Markov chain Monte Carlo to sample from posterior distributions was the key innovation which made Bayesian data analysis practical. Notoriously, however, MCMC is hard to tune, hard to diagnose, and hard to parallelize. This pedagogical note explores variants on a universal {\em non}-Markov-chain Monte Carlo scheme for sampling from posterior distributions. The basic idea is to draw parameter values from the prior distributions, evaluate the likelihood of each draw, and then copy that draw a number of times proportional to its likelihood. The distribution after copying is an approximation to the posterior which becomes exact as the number of initial samples goes to infinity; the convergence of the approximation is easily analyzed, and is uniform over Glivenko-Cantelli classes. While not {\em entirely} practical, the schemes are straightforward to implement (a few lines of R), easily parallelized, and require no rejection, burn-in, convergence diagnostics, or tuning of any control settings. I provide references to the prior art which deals with some of the practical obstacles, at some cost in computational and analytical simplicity.
