Particle Learning of Gaussian Process Models for Sequential Design and Optimization
Particle Learning of Gaussian Process Models for Sequential Design and Optimization
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DOI：
10.1198/jcgs.2010.09171
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发表时间：
2011-03-01
影响因子：
2.4
通讯作者：
Polson, Nicholas G.
中科院分区：
文献类型：
--
作者：
Gramacy, Robert B.;Polson, Nicholas G.
我们开发了一种基于仿真的方法来在线更新高斯过程回归和分类模型。我们的方法利用时序蒙特卡罗为这些模型提供了相对于已建立的MCMC替代方案的快速时序设计算法。后者对于顺序设计来说不太理想，因为它必须重新启动并迭代以包含每个新设计点。我们展示了SMC方法的一些吸引人的集成方面，并展示了如何通过粒子实现主动学习启发式来优化噪声函数或在线探索分类边界。补充材料，包括一个R包，可以在网上找到。
We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a fast sequential design algorithm for these models relative to the established MCMC alternative. The latter is less ideal for sequential design since it must be restarted and iterated to convergence with the inclusion of each new design point. We illustrate some attractive ensemble aspects of our SMC approach, and show how active learning heuristics may be implemented via particles to optimize a noisy function or to explore classification boundaries online. Supplemental material, including an R package, is available online.