为最大预期效用提供生成贝叶斯计算
Nick Polson1, Fabrizio Ruggeri2, Vadim Sokolov3
1Booth School of Business, University of Chicago, Chicago, IL 60637, USA.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
生成贝叶斯计算 (GBC) 提供了一种新的,无密度的方法,以高效地估计最大预期效用 (MEU). 该方法使用深度量子神经网络来进行最佳的投资组合分配和风险评估.
科学领域:
- 计算统计学 计算统计学
- 决策理论 决策理论
- 机器学习 机器学习
背景情况:
- 最大预期效用 (MEU) 是决策理论的一个核心概念.
- 对于MEU,现有的计算方法可能是低效的.
- 无概率方法提供灵活性,但需要专门的方法.
研究的目的:
- 开发一种高效,无密度的计算方法来估计MEU.
- 介绍一种新的生成贝叶斯计算 (GBC) 方法.
- 将该方法应用于一个最佳的投资组合分配问题.
主要方法:
- 建议使用基于量子的无密度生成方法.
- 深度量子神经网络用于模拟分布式实用程序.
- 监督学习问题是以非参数回归形式制定的.
主要成果:
- 拟议的方法有效地估计了后置量数的边际值的预期效用.
- 该方法被证明是无密度的,并且在计算上具有优势.
- 通过贝叶斯式学习和电源效用解决了一个最佳的投资组合分配问题.
结论:
- 生成贝叶斯计算为MEU提供了一个有效的解决方案.
- 无密度量子基方法提供了显著的计算优势.
- 未来的研究可以探索决策和风险分析的进一步应用.
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