使用吉布斯自调器将本地步骤大小适应性纳入无转向采样器
Nawaf Bou-Rabee1, Bob Carpenter2, Tore Selland Kleppe3
1Department of Mathematical Sciences, Rutgers University, Piscataway, New Jersey 08854-8019, USA.
The Journal of chemical physics
|August 28, 2025
概括
本研究引入了一种新的方法来调整无U转取样器 (NUTS) 中的步骤大小. 这种方法确保了复杂贝叶斯推理问题的可逆性和提升抽样效率.
科学领域:
- 计算统计
- 贝叶斯推理
- 马尔科夫链蒙特卡洛方法
背景情况:
- 由于相互依存的调整参数,在无转向采样器 (NUTS) 中调整步骤大小是复杂的.
- 最佳路径长度的确定需要一个固定的步骤大小,而理想的步骤大小取决于路径错误.
- 确保采样器可逆性增加了调过程的复杂性.
研究的目的:
- 在NUTS算法中开发一种新的局部级别调整方法.
- 在调整过程中确保NUTS采样器的可逆性.
- 使用NUTS提高贝叶斯推理的效率和可靠性.
主要方法:
- 提出一个步骤大小适应方法作为吉布斯自调 (GIST) 框架的实例.
- 开发了一种保证采样器可逆性的方法.
- 接受概率完全取决于步骤大小的条件分布.
主要成果:
- 拟议的方法有效地在NUTS中适应步骤大小.
- 在整个适应过程中保证可逆性.
- 这种方法在具有挑战性的分布上表现出有效性,如尼尔的道和高维的正常分布.
结论:
- 基于GIST的新型NUTS适应方法成功地解决了相互依赖的调整参数的挑战.
- 这种方法确保了可逆性,并改善了复杂情景中的采样性能.
- 这种方法为贝叶斯计算提供了更强大,更有效的工具.
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