FLARE MCMC：基于保真度的自适应分层递归马尔可夫链蒙特卡洛提案
FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
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中文总结 AI 辅助
FLARE MCMC是一种多保真度分层MCMC方法，通过利用低保真度似然提升混合效率，在水文学、宇宙学等领域相同计算时间下可获得更大有效样本量，性能更优。
中文摘要 AI 辅助
马尔可夫链蒙特卡洛（MCMC）仅需具备评估似然的能力，因此是复杂模型推断的常用技术，但它混合速率较慢，需生成大量样本才能获得良好估计，整体计算成本较高。FLARE MCMC是一种多保真度分层MCMC方法，利用真实似然计算的低保真度近似值来提升混合效率，实现更快的整体性能。在涉及可调节分辨率或精度的模拟模型的科学与工程应用中，这类低保真度似然通常是可用的。我们的技术采用递归分层链，具有简单的层调优机制，无需似然满足特定形式或内部数学结构。实验表明，在水文学、宇宙学等不同科学领域，FLARE MCMC在相同计算时间下能获得更大的有效样本量。
英文摘要
Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models. However, it can have a slow mixing rate, requiring the generation of many samples to obtain good estimates and an overall high computational cost. FLARE MCMC is a multi-fidelity layered MCMC method that exploits lower-fidelity approximations of the true likelihood calculation to improve mixing and leads to overall faster performance. Such lower-fidelity likelihoods are commonly available in scientific and engineering applications where the model involves a simulation whose resolution or accuracy can be tuned. Our technique uses recursive, layered chains with simple layer tuning; it does not require the likelihood to take any form or have any particular internal mathematical structure. We demonstrate experimentally that FLARE MCMC achieves larger effective sample sizes for the same computational time across different scientific domains including hydrology and cosmology.
发表机构
- University of California, Riverside（加利福尼亚大学河滨分校）
- University of Michigan（密歇根大学）
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