A comparative evaluation of stochastic-based inference methods for Gaussian process models
A comparative evaluation of stochastic-based inference methods for Gaussian process models
复制标题
DOI：
10.1007/s10994-013-5388-x
复制
发表时间：
2013-10-01
期刊：
影响因子：
7.5
通讯作者：
Girolami, M.
中科院分区：
文献类型：
--
作者：
Filippone, M.;Zhong, M.;Girolami, M.
高斯过程模型以其灵活的建模能力和可解释性在数据分析中得到了广泛的应用。GP模型的完全贝叶斯处理在分析上是难以处理的，因此有必要诉诸于确定性或随机近似。本文主要研究基于随机的推理技术。在讨论了与GP模型的完全贝叶斯处理相关的挑战之后，提出了一些基于马尔可夫链蒙特卡罗方法的推理策略并进行了严格的评估。特别是，基于高效参数化和高效提议机制的策略在模拟数据和真实数据上的收敛速度、采样效率和计算成本进行了广泛的比较。
Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling capabilities and interpretability. The fully Bayesian treatment of GP models is analytically intractable, and therefore it is necessary to resort to either deterministic or stochastic approximations. This paper focuses on stochastic-based inference techniques. After discussing the challenges associated with the fully Bayesian treatment of GP models, a number of inference strategies based on Markov chain Monte Carlo methods are presented and rigorously assessed. In particular, strategies based on efficient parameterizations and efficient proposal mechanisms are extensively compared on simulated and real data on the basis of convergence speed, sampling efficiency, and computational cost.