通过专家获得的数据对隐藏的马尔科夫模型进行最佳推断
Amirhossein Ravari1, Seyede Fatemeh Ghoreishi2, Mahdi Imani1
1Department of Electrical and Computer Engineering at Northeastern University.
本研究引入了一种新的方法,通过整合专家知识来推断隐藏的马尔科夫模型 (HMM),提高导航和网络安全等复杂系统的准确性. 该方法模拟专家行为,以增强超越传统时间方法的数据分析.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 系统生物学 系统生物学
背景情况:
- 推断隐藏马尔科夫模型 (HMM) 的传统方法主要依赖于时间数据动态.
- 专家获得的数据,包括导航,网络安全和生物学等领域的人类决策,提供了丰富的见解,但在HMM推断中往往未得到充分利用.
- 现有的方法缺乏有效纳入专家知识的机制,限制了模型的准确性和适用性.
研究的目的:
- 开发一种新的HMM推理方法,将专家知识与时间数据相结合.
- 将专家行为建模为一个不完美的强化学习代理,以量化他们对系统的理解.
- 通过各种推断标准和复杂的现实应用来证明该方法的有效性.
主要方法:
- 通过使用强化学习原则模拟他们的决策过程,将专家知识纳入.
- 开发一种结论方法,结合动态编程和最佳递归贝叶斯估计.
- 量化专家对系统模型的看法,以增强时间数据分析.
主要成果:
- 拟议的方法有效地将专家的见解与HMM推理相结合,性能优于传统方法.
- 已证明适用于各种推断标准,包括最大概率和最大后期推断.
- 通过对基准问题和生物网络进行全面的数值实验进行验证,展示强大的性能.
结论:
- 可以有效地利用专家知识来增强HMM推断,从而产生更准确的系统模型.
- 拟议的方法提供了一种原则性的方法,将时间数据与不完美的专家指导相结合.
- 这种方法在自主系统,网络安全和生物网络分析等领域具有很大的应用潜力.
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