在因子图中自动化模型比较
Bart van Erp1, Wouter W L Nuijten1, Thijs van de Laar1
1Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种使用消息传递进行贝叶斯模型比较的自动化方法. 这种方法简化了复杂的贝叶斯分析,加速了概率模型的设计周期.
科学领域:
- 计算统计学 计算统计学
- 机器学习 机器学习
- 概率模型可能模型
背景情况:
- 自动化贝叶斯状态和参数估计在概率编程语言中很常见.
- 贝叶斯模型比较至关重要,但往往是手动的,容易出错,耗时.
- 目前的方法限制了贝叶斯模型比较的有效应用.
研究的目的:
- 为了自动化贝叶斯模型的平均,选择和组合.
- 为推断和模型比较开发一个统一的消息传递框架.
- 为了简化贝叶斯模型设计和分析工作流程.
主要方法:
- 使用福尼式的因子图表传递信息.
- 引入一个自定义的混合节点,以进行高效的模型比较.
- 执行参数/状态推断和模型比较同时使用尺度因子.
主要成果:
- 贝叶斯模型平均,选择和组合的高效自动化.
- 通过消息传递同时执行推断和模型比较.
- 证明了扩展到等级和时间先验的能力.
结论:
- 建议的消息传递方法有效地自动化了贝叶斯模型比较.
- 这种方法显著缩短了模型设计周期.
- 该框架有助于对复杂的,时间变化的过程进行建模.
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