利用 Nii-C 实现自动并行回火马尔可夫链蒙特卡洛

Sheng Jin, Wenxin Jiang, Dong-Hong Wu
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摘要

由于现代天文学中常见的高维度或多模态性,贝叶斯后验取样可能具有挑战性。一些基于不同采样算法的公开代码可以解决这些复杂模型的问题,但代码执行的效率和速度并不总是足够高。本文介绍了一种 C 语言通用代码 Nii-C,它实现了一个自动并行调节马尔可夫链蒙特卡罗的框架。这里所说的自动是指在采样过程的初始阶段,可以通过控制系统设置确保高效并行调节过程的参数。自动调整参数由两部分组成,即所有平行回火马尔可夫链的温度梯度和所有平行回火链上所有模型参数的建议分布。为了减少编译过程中的依赖性并提高代码执行速度,Nii-C 代码完全由 C 语言构建,并使用消息传递接口协议进行并行化,以优化并行采样的效率。这些实现有助于高维和多模态分布采样的快速收敛,以及加快代码执行时间。Nii-C 代码的采样效率高、执行速度快,因此可用于各种研究领域,对复杂分布进行追踪。本文将介绍 Nii-C 代码的一些应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Parallel Tempering Markov Chain Monte Carlo with Nii-C
Due to the high dimensionality or multimodality that is common in modern astronomy, sampling Bayesian posteriors can be challenging. Several publicly available codes based on different sampling algorithms can solve these complex models, but the execution of the code is not always efficient or fast enough. The article introduces a C language general-purpose code, Nii-C, that implements a framework of automatic parallel tempering Markov Chain Monte Carlo. Automatic in this context means that the parameters that ensure an efficient parallel tempering process can be set by a control system during the inital stages of a sampling process. The autotuned parameters consist of two parts, the temperature ladders of all parallel tempering Markov Chains and the proposal distributions for all model parameters across all parallel tempering chains. In order to reduce dependencies in the compilation process and increase the code’s execution speed, Nii-C code is constructed entirely in the C language and parallelized using the message-passing interface protocol to optimize the efficiency of parallel sampling. These implementations facilitate rapid convergence in the sampling of high-dimensional and multimodal distributions, as well as the expeditious code execution time. The Nii-C code can be used in various research areas to trace complex distributions due to its high sampling efficiency and quick execution speed. This article presents a few applications of the Nii-C code.
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