Data free inference with processed data products

Chowdhary K.; Najm H. N.

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Data free inference with processed data products

机译：处理数据产品的无数据推断

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We consider the context of probabilistic inference of model parameters given error bars or confidence intervals on model output values, when the data is unavailable. We introduce a class of algorithms in a Bayesian framework, relying on maximum entropy arguments and approximate Bayesian computation methods, to generate consistent data with the given summary statistics. Once we obtain consistent data sets, we pool the respective posteriors, to arrive at a single, averaged density on the parameters. This approach allows us to perform accurate forward uncertainty propagation consistent with the reported statistics.

机译：当数据不可用时，我们考虑在给定误差条或模型输出值的置信区间的情况下模型参数的概率推断的上下文。我们在贝叶斯框架中引入一类算法，该算法依靠最大熵参数和近似贝叶斯计算方法来生成具有给定摘要统计信息的一致数据。一旦获得一致的数据集，我们便合并各自的后代，以得出参数的单个平均密度。这种方法使我们能够执行与报告的统计数据一致的准确的前向不确定性传播。

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《Statistics and computing》 |2016年第2期|149-169|共21页
作者
Chowdhary K.; Najm H. N.;
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作者单位

Sandia Natl Labs, Livermore, CA USA;

Sandia Natl Labs, Livermore, CA USA;

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原文格式 PDF
正文语种 eng
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关键词
Uncertainty quantification; Bayesian inference; Markov Chain Monte Carlo; Approximate Bayesian computation; Maximum entropy; Missing information;

机译：不确定性量化贝叶斯推断马尔可夫链蒙特卡洛近似贝叶斯计算最大熵缺失信息;

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Data free inference with processed data products

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