似然范式下的不确定性分析与复合假设
Uncertainty analysis and composite hypothesis under the likelihood paradigm
摘要：
模型的正确使用和解释取决于几个步骤，其中两个步骤是通过参数估计进行校准和不确定性分析。在生物学文献中，这些步骤很少一起讨论，但它们可以被视为同一个难题的合适部分。特别是，不确定性估计的分析程序可能会掩盖来自具有稳定结构但数据不足的模型的高度不确定性。在似然方法下，不确定性估计问题与复合假设问题密切相关。本文简要介绍了似然统计学派的历史背景，分析了似然律与复合假设问题之间的复杂关系以及现有的解决方法，提出了一种利用数据信息进行模型不确定性估计的综合方法。我们认为，这种方法是直观的吸引力下的可能性范式。
英文摘要：
The correct use and interpretation of models depends on several steps, two of which being the calibration by parameter estimation and the analysis of uncertainty. In the biological literature, these steps are seldom discussed together, but they can be seen as fitting pieces of the same puzzle. In particular, analytical procedures for uncertainty estimation may be masking a high degree of uncertainty coming from a model with a stable structure, but insufficient data. Under a likelihoodist approach, the problem of uncertainty estimation is closely related to the problem of composite hypothesis. In this paper, we present a brief historical background on the statistical school of Likelihoodism, and examine the complex relations between the law of likelihood and the problem of composite hypothesis, together with the existing proposals for coping with it. Then, we propose a new integrative methodology for the uncertainty estimation of models using the information in the collected data. We argue that this methodology is intuitively appealing under a likelihood paradigm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
