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Efficient construction of Bayes optimal designs for stochastic process models

机译：高效建设随机工艺模型的贝叶斯最优设计

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Stochastic process models are now commonly used to analyse complex biological, ecological and industrial systems. Increasingly there is a need to deliver accurate estimates of model parameters and assess model fit by optimizing the timing of measurement of these processes. Standard methods to construct Bayes optimal designs, such the well known Muller algorithm, are computationally intensive even for relatively simple models. A key issue is that, in determining the merit of a design, the utility function typically requires summaries of many parameter posterior distributions, each determined via a computer-intensive scheme such as MCMC. This paper describes a fast and computationally efficient scheme to determine optimal designs for stochastic process models. The algorithm compares favourably with other methods for determining optimal designs and can require up to an order of magnitude fewer utility function evaluations for the same accuracy in the optimal design solution. It benefits from being embarrassingly parallel and is ideal for running on multi-core computers. The method is illustrated by determining different sized optimal designs for three problems of increasing complexity.
机译：现在通常用于分析复杂的生物，生态和工业系统的随机过程模型。越来越需要通过优化这些过程的测量时间来提供模型参数的准确估计，并评估模型适合。构建贝叶斯最佳设计的标准方法，这种众所周知的Muller算法，即使对于相对简单的模型，也是计算密集的。关键问题是，在确定设计的优点时，实用程序函数通常需要许多参数后部分布的总结，每个分布是通过诸如MCMC的计算机密集型方案确定。本文介绍了一种快速和计算上有效的方案，用于确定随机过程模型的最佳设计。该算法利用其他方法对确定最佳设计的方法有利地进行比较，并且可以在最佳设计解决方案中最多需要较少的实用程序函数评估顺序。它受益于令人尴尬的平行，并且是在多核计算机上运行的理想选择。通过确定不同尺寸的最佳设计来说明该方法，用于增加复杂性的三个问题。

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