The use of a single pseudo-sample in approximate Bayesian computation
The use of a single pseudo-sample in approximate Bayesian computation
复制标题
在近似贝叶斯计算中使用单个伪样本
作者：
L. Bornn;N. Pillai;Aaron Smith;D. Woodard
我们分析了近似贝叶斯计算（ABC）的计算效率，它近似的似然函数从相关的模型中绘制伪样本。对于ABC的拒绝采样版本，已知与采用基于单个伪样本的高方差估计相比，多个伪样本不能显著增加（并且可以显著降低）算法的效率。我们表明，这一结论也适用于马尔可夫链蒙特卡罗版本的ABC，这意味着它是不必要的调整伪样本的数量在ABC-MCMC中使用。这一结论与粒子MCMC方法相反，对于粒子MCMC方法，增加粒子的数量可以在计算效率上提供很大的增益。
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For the rejection sampling version of ABC, it is known that multiple pseudo-samples cannot substantially increase (and can substantially decrease) the efficiency of the algorithm as compared to employing a high-variance estimate based on a single pseudo-sample. We show that this conclusion also holds for a Markov chain Monte Carlo version of ABC, implying that it is unnecessary to tune the number of pseudo-samples used in ABC-MCMC. This conclusion is in contrast to particle MCMC methods, for which increasing the number of particles can provide large gains in computational efficiency.
DOI：
10.1214/14-aap1022
发表时间：
2015
期刊：
The Annals of Applied Probability
影响因子：
--
作者：
Andrieu C
通讯作者：
Andrieu C
影响因子：
1.8
作者：
C. Andrieu;M. Vihola
通讯作者：
C. Andrieu;M. Vihola
影响因子：
10.7
作者：
Pritchard, JK;Seielstad, MT;Feldman, MW
通讯作者：
Feldman, MW