Information criteria for astrophysical model selection
Information criteria for astrophysical model selection
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DOI：
10.1111/j.1745-3933.2007.00306.x
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发表时间：
2007-05-01
影响因子：
4.8
通讯作者：
Liddle, Andrew R.
中科院分区：
文献类型：
--
作者：
Liddle, Andrew R.
模型选择是区分竞争模型的问题，可能具有不同数量的参数。统计学文献包含两套不同的工具，一套是基于信息理论的工具，如赤池信息准则（AIC），另一套是基于贝叶斯推理的工具，如贝叶斯证据和贝叶斯信息准则（BIC）。偏差信息准则结合了这两种传统的思想;它很容易从蒙特卡洛后验样本计算，与AIC和BIC不同，它允许参数退化。我描述的信息标准的属性，并作为一个例子计算他们从威尔金森微波各向异性探测器3年的数据为几个宇宙学模型。我发现，目前的信息理论和贝叶斯方法给出了显着不同的结论，从这些数据。
Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from Wilkinson Microwave Anisotropy Probe 3-yr data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.