Learning stochastic model discrepancy

M. Plumlee, H. Lam
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引用次数: 5

Abstract

The vast majority of stochastic simulation models are imperfect in that they fail to fully emulate the entirety of real dynamics. Despite this, these imperfect models are still useful in practice, so long as one knows how the model is inexact. This inexactness is measured by a discrepancy between the proposed stochastic model and a true stochastic distribution across multiple values of some decision variables. In this paper, we propose a method to learn the discrepancy of a stochastic simulation using data collected from the system of interest. Our approach is a novel Bayesian framework that addresses the requirements for estimation of probability measures.
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学习随机模型差异
绝大多数随机模拟模型都是不完美的,因为它们不能完全模拟真实动态的全部。尽管如此,这些不完美的模型在实践中仍然是有用的,只要人们知道模型是如何不精确的。这种不精确性是通过所提出的随机模型与一些决策变量的多个值之间的真实随机分布之间的差异来衡量的。在本文中,我们提出了一种利用从感兴趣的系统收集的数据来学习随机模拟的差异的方法。我们的方法是一种新颖的贝叶斯框架,解决了估计概率度量的要求。
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