.png){kind=icon}
返回
Approximate Bayesian Computation: A Nonparametric Perspective
.png){kind=icon}
OA
AI
DOI：10.1198/jasa.2010.tm09448.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics s(obs) from the data and simulating summary statistics for different values of the parameter Theta. The posterior distribution is then approximated by an estimator of the conditional density g(Theta vertical bar s(obs)). In this paper, we derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a fewer number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better performance when there is a nearly homoscedastic relationship between the summary statistics and the parameter of interest. To make this relationship as homoscedastic as possible, we propose to use transformations of the summary statistics. In different examples borrowed from the population genetics and epidemiological literature, we show the potential of the methods with adjustment and of the transformations of the summary statistics. Supplemental materials containing the details of the proofs are available online.
Keyword:
Conditional density estimation
Implicit statistical model
Kernel regression
Local polynomial
Simulation-based inference
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
暂无机构信息
引用论文
分享收藏Inferring the parameters of the neutral theory of biodiversity using phylogenetic information and implications for tropical forests.png)
Ecology Letters
Ecology LettersIF7.9
PREAI
分享收藏Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without LikelihoodGENETICSOAAI
GENETICSIF5.1
OAAI分享收藏The complement system: a pathway linking host defence and adipocyte biology补体系统: 连接宿主防御和脂肪细胞生物学的途径
PREAI
分享收藏
查看更多内容