Approximately sufficient statistics and bayesian computation.
Approximately sufficient statistics and bayesian computation.
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DOI：
10.2202/1544-6115.1389
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发表时间：
2008-01-01
影响因子：
0.9
通讯作者：
Marjoram, Paul
中科院分区：
文献类型：
--
作者：
Joyce, Paul;Marjoram, Paul
高维数据集的分析往往被迫依赖于精心选择的汇总统计量。目前缺乏一种以健全的理论框架为基础的选择此类统计数据的系统方法。在本文中，我们开发了一个连续的计划，评分统计数据，根据他们是否列入分析将大大提高推理的质量。我们的方法可以应用于高维数据集，精确的似然方程是不可能的。我们用一系列来自遗传学的例子来说明我们方法的潜力。总之，在精心选择的汇总统计量非常重要的背景下，我们试图将“好”放入“选择”中。'
The analysis of high-dimensional data sets is often forced to rely upon well-chosen summary statistics. A systematic approach to choosing such statistics, which is based upon a sound theoretical framework, is currently lacking. In this paper we develop a sequential scheme for scoring statistics according to whether their inclusion in the analysis will substantially improve the quality of inference. Our method can be applied to high-dimensional data sets for which exact likelihood equations are not possible. We illustrate the potential of our approach with a series of examples drawn from genetics. In summary, in a context in which well-chosen summary statistics are of high importance, we attempt to put the 'well' into 'chosen.'