Current challenges in Bayesian model choice

被引:0
|
作者
Clyde, M. A. [1 ]
Berger, J. O. [1 ]
Bullard, F. [1 ]
Ford, E. B. [2 ]
Jefferys, W. H. [3 ]
Luo, R. [1 ]
Paulo, R. [4 ]
Loredo, T. [5 ]
机构
[1] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27708 USA
[2] Univ Calif Berkeley, Dept Astron, Berkeley, CA 94720 USA
[3] Univ Texas Austin, Dept Astron, Austin, TX 78712 USA
[4] Univ Bristol, Dept Math, Bristol BS8 1TW, Avon, England
[5] Cornell Univ, Dept Astron, Ithaca, NY 14853 USA
关键词
MONTE-CARLO METHODS; NORMALIZING CONSTANTS; VARIABLE SELECTION; POSTERIOR DISTRIBUTIONS; MARGINAL LIKELIHOOD; ESTIMATING RATIOS; DENSITIES;
D O I
暂无
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Model selection (and the related issue of model uncertainty) arises in many astronomical problems, and, in particular, has been one of the focal areas of the Exoplanet working group under the SAMSI (Statistics and Applied Mathematical Sciences Institute) Astrostatistcs Exoplanet program. We provide an overview of the Bayesian approach to model selection and highlight the challenges involved in implementing Bayesian model choice in four stylized problems. We review some of the current methods used by statisticians and astronomers and present recent developments in the area. We discuss the applicability, computational challenges, and performance of suggested methods and conclude with recommendations and open questions.
引用
收藏
页码:224 / +
页数:3
相关论文
共 50 条