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近似贝叶斯计算:是什么,为什么以及怎么样?

Approximate Bayesian Computation: What, Why and How?
课程网址: http://videolectures.net/aistats2010_tavare_abc/  
主讲教师: Simon Tavaré
开课单位: 美国国立卫生研究院
开课时间: 2010-06-17
课程语种: 英语
中文简介:
近似贝叶斯计算(abc)是对由难以处理的相似性确定的后验分布模拟观测困难的响应。该方法利用了这样一个事实:虽然在复杂的概率模型中可能无法计算出相似性,但通常很容易从中模拟观测结果。ABC最简单的形式如下:(i)模拟先前的参数;(i i)用该参数模拟模型的观测;(i i i)如果模拟观测值与观测数据足够接近,接受该参数。魔术,以及潜在灾难的来源,在步骤(iii)中。这篇演讲将概述我们所知道的(而不是!)介绍了ABC方法及其在化石记录和干细胞生物学中的应用。
课程简介: Approximate Bayesian Computation (ABC) arose in response to the difficulty of simulating observations from posterior distributions determined by intractable likelihoods. The method exploits the fact that while likelihoods may be impossible to compute in complex probability models, it is often easy to simulate observations from them. ABC in its simplest form proceeds as follows: (i) simulate a parameter from the prior; (ii) simulate observations from the model with this parameter; (iii) accept the parameter if the simulated observations are close enough to the observed data. The magic, and the source of potential disasters, is in step (iii). This talk will outline what we know (and don't!) about ABC and illustrate the methods with applications to the fossil record and stem cell biology.
关 键 词: 贝叶斯方程; 概率模型; 计算; 参数
课程来源: 视频讲座网
最后编审: 2020-03-26:chenxin
阅读次数: 174