A note on approximating ABC‐MCMC using flexible classifiers
A note on approximating ABC‐MCMC using flexible classifiers
复制标题
关于使用灵活分类器逼近 ABC-MCMC 的注意事项
作者：
Kim Cuc Pham;D. Nott;S. Chaudhuri
在似然难以处理的情况下，考虑了一种逼近马尔可夫链蒙特卡罗算法的方法。该方法基于将 Metropolis-Hastings 接受概率中的似然比解释为贝叶斯分类规则中的几率，以区分观察到的数据是使用建议参数值还是当前参数值生成的。使用来自模型的模拟数据和能够处理高维特征向量的现代灵活分类器来近似贝叶斯规则，会产生新的近似贝叶斯计算程序，该程序能够在高维汇总统计中表现良好。在中小型问题中，甚至可以完全省去汇总统计。 Wood 的合成可能性对应于该框架中二次判别分析的分类。版权所有 © 2014 约翰·威利父子有限公司
A method for approximating Markov chain Monte Carlo algorithms is considered in the setting where the likelihood is intractable. The approach is based on interpreting the likelihood ratio in the Metropolis–Hastings acceptance probability as the odds in the Bayes classification rule for distinguishing whether the observed data were generated using the proposal parameter value or the current one. Approximating the Bayes rule using simulated data from the model and modern flexible classifiers capable of dealing with high‐dimensional feature vectors results in new approximate Bayesian computation procedures that are able to perform well with high‐dimensional summary statistics. In problems of small to moderate size, it may even be possible to dispense with summary statistics altogether. The synthetic likelihood of Wood corresponds to classification by quadratic discriminant analysis in this framework. Copyright © 2014 John Wiley & Sons, Ltd.
影响因子：
10.7
作者：
Pritchard, JK;Seielstad, MT;Feldman, MW
通讯作者：
Feldman, MW
影响因子：
2.7
作者：
Beaumont, Mark A.;Cornuet, Jean-Marie;Robert, Christian P.
通讯作者：
Robert, Christian P.