普遍性质的稳定性对马尔科夫链蒙特卡洛算法的扰动
Matteo Bacci1, Claudio Bonati1
1INFN Sezione di Pisa, dell'Università di Pisa, Dipartimento di Fisica , Largo Pontecorvo 3, I-56127 Pisa, Italy.
Physical review. E
|October 21, 2025
概括
连续相变的普遍性质在马尔科夫链蒙特卡洛 (MCMC) 算法扰动下保持稳定. 关键指数和缩放在很大程度上不受影响,即使有显著的算法变化.
科学领域:
- 统计力学就是统计力学.
- 计算物理学的计算物理.
- 阶段过渡 阶段过渡
背景情况:
- 连续相位过渡表现出由临界指数支配的普遍性质.
- 马尔科夫链蒙特卡洛 (MCMC) 算法对于模拟这些系统至关重要.
- 算法干扰可能会影响模拟的通用属性的准确性.
研究的目的:
- 为了研究在连续相位过渡中普遍性质的稳定性.
- 评估MCMC算法中扰动对关键行为的影响.
- 为了确定是否普遍的属性保持强大的对算法不准确性.
主要方法:
- 三维XY模型的数值模拟.
- 实施本地 (单个站点大都市) 和全球 (单个集群) MCMC更新.
- 引入确定性 (类似切断) 和随机性 (接受概率) 扰动.
主要成果:
- 普遍性质证明了对大多数MCMC扰动的显著稳定性.
- 临界指数和缩放曲线与标准XY模型保持一致.
- 单个集群更新中的显著干扰导致了偏差,但缩放校正掩盖了精确的分析.
结论:
- 连续相位转换的通用性质通常对MCMC算法扰动具有强度.
- 三维XY模型在各种算法条件下表现出稳定的关键行为.
- 需要对全球更新中出现大量截断错误的情况进行进一步调查,以确定普遍性类.
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