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Comment on Article by Schmidl et al.
被引:0
|作者:
Girolami, Mark
[1
]
Mira, Antonietta
[2
]
机构:
[1] UCL, Dept Stat Sci, London WC1E 6BT, England
[2] Univ Lugano, Inst Finance, Lugano, Switzerland
来源:
基金:
美国国家科学基金会;
关键词:
D O I:
10.1214/13-BA801B
中图分类号:
O1 [数学];
学科分类号:
0701 ;
070101 ;
摘要:
We thoroughly enjoyed reading this paper and are delighted to contribute to its discussion. The authors have been particularly innovative in drawing upon quite di erent areas of statistical inference to devise ecient Markov transition kernels for complex posterior distributions. Landmark papers provoke discussion and raise many open questions leading to potentially fruitful new avenues of investigation and this paper is no exception. © 2013 International Society for Bayesian Analysis.
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页码:27 / 32
页数:6
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