A minimally informative likelihood for decision analysis: Illustration and robustness
A minimally informative likelihood for decision analysis: Illustration and robustness
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决策分析的最小信息可能性：说明和稳健性
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
B. Clarke
中科院分区：
文献类型：
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作者：
A. Yuan;B. Clarke
作者讨论了一类似然函数，涉及弱假设的数据生成机制。当难以为数据提出模型时，这些可能性可能是合适的。给出了这些似然性的性质，并展示了如何使用Blahut-Arimoto算法对其进行数值计算。然后，作者展示了这些可能性如何使用没有合理物理模型的数据集进行有用的推断。通过这些可能性允许的广泛的鲁棒性分析，增强了推理的可扩展性。
The authors discuss a class of likelihood functions involving weak assumptions on data generating mechanisms. These likelihoods may be appropriate when it is difficult to propose models for the data. The properties of these likelihoods are given and it is shown how they can be computed numerically by use of the Blahut‐Arimoto algorithm. The authors then show how these likelihoods can give useful inferences using a data set for which no plausible physical model is apparent. The plausibility of the inferences is enhanced by the extensive robustness analysis these likelihoods permit.