Bayesian optimization with informative parametric models via sequential Monte Carlo

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rafael Oliveira, R. Scalzo, R. Kohn, Sally Cripps, Kyle Hardman, J. Close, Nasrin Taghavi, C. Lemckert
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引用次数: 0

Abstract

Abstract Bayesian optimization (BO) has been a successful approach to optimize expensive functions whose prior knowledge can be specified by means of a probabilistic model. Due to their expressiveness and tractable closed-form predictive distributions, Gaussian process (GP) surrogate models have been the default go-to choice when deriving BO frameworks. However, as nonparametric models, GPs offer very little in terms of interpretability and informative power when applied to model complex physical phenomena in scientific applications. In addition, the Gaussian assumption also limits the applicability of GPs to problems where the variables of interest may highly deviate from Gaussianity. In this article, we investigate an alternative modeling framework for BO which makes use of sequential Monte Carlo (SMC) to perform Bayesian inference with parametric models. We propose a BO algorithm to take advantage of SMC’s flexible posterior representations and provide methods to compensate for bias in the approximations and reduce particle degeneracy. Experimental results on simulated engineering applications in detecting water leaks and contaminant source localization are presented showing performance improvements over GP-based BO approaches.
基于序列蒙特卡罗的具有信息参数模型的贝叶斯优化
摘要贝叶斯优化(BO)是一种成功的优化昂贵函数的方法,其先验知识可以通过概率模型来指定。由于其表现力和可处理的闭式预测分布,高斯过程(GP)代理模型一直是推导BO框架时的默认选择。然而,作为非参数模型,当应用于科学应用中的复杂物理现象建模时,GP在可解释性和信息能力方面提供的很少。此外,高斯假设还限制了GP对感兴趣的变量可能高度偏离高斯性的问题的适用性。在本文中,我们研究了BO的另一种建模框架,该框架利用序列蒙特卡罗(SMC)对参数模型进行贝叶斯推理。我们提出了一种BO算法来利用SMC的灵活后验表示,并提供了补偿近似中的偏差和减少粒子退化的方法。在检测漏水和污染物源定位方面的模拟工程应用的实验结果表明,与基于GP的BO方法相比,性能有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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