使用贝叶斯先验来克服隐藏马尔科夫模型中的不可识别性问题
Jan L Münch1, Ralf Schmauder1, Fabian Paul2
1Institute of Physiology II, Jena University Hospital, Friedrich Schiller University, Jena 07743, Germany.
bioRxiv : the preprint server for biology
|July 14, 2025
概括
用精心挑选的先验进行贝叶斯推理,可以改善生物分子的隐藏马尔科夫模型 (HMM). 这种方法提高了准确性,减少了不确定性,即使是低质量的数据.
科学领域:
- 计算生物学是一种计算生物学.
- 生物物理学的生物物理.
- 统计建模 统计建模
背景情况:
- 隐藏的马尔科夫模型 (HMM) 对于分析生物分子数据至关重要,但参数不可识别性阻碍了准确的推断.
- 由于这些模型的复杂性,最大概率和贝叶斯推理方法都面临着挑战.
研究的目的:
- 调查先前分布对HMM在参数不可识别性背景下的贝叶斯推理的影响.
- 为了优化从联结离子道的补丁数据的推断.
主要方法:
- 应用贝叶斯推理,重点关注具有最小信息性的先前分布.
- 研究了将参数空间限制在物理动机限制的效果.
- 对于联结事件的有限合作性的内置假设.
主要成果:
- 至少有信息的先验增加了推断的准确性,减少了不确定性.
- 较强的先前假设,如物理动机限制,确保复杂的HMM足够适当的后部.
- 有限合作性先验偏向于非合作性,同时允许基于数据的推断.
结论:
- 对于具有不可识别参数的HMM中强大的贝叶斯推理,先前分布是必不可少的.
- 提议的先前策略使得即使使用质量明显较低的数据集,也可以得出有意义的推断.
- 这项工作促进了HMM在生物物理建模和数据分析中的应用.
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