论文标题
无可能回归的高斯流程
Likelihood-Free Gaussian Process for Regression
论文作者
论文摘要
高斯过程回归可以灵活地表示有关可能性的足够信息的兴趣参数的后验分布。但是，在某些情况下，我们对概率模型的了解很少。例如，在投资金融工具时，现金流的概率模型通常是未知的。在本文中，我们提出了一个名为“无似然高斯过程（LFGP）”的新型框架，该框架允许表示可扩展问题的兴趣参数的后验分布，而无需直接设置其可能性函数。 LFGP建立了群集，其中利益参数的值可以被视为近似相同的群体，并且使用最大似然估计量的渐近正态性近似于每个集群中兴趣参数的可能性与高斯。我们预计，提出的框架将对无可能建模产生重大贡献，尤其是通过减少概率模型的假设和可扩展问题的计算成本。
Gaussian process regression can flexibly represent the posterior distribution of an interest parameter given sufficient information on the likelihood. However, in some cases, we have little knowledge regarding the probability model. For example, when investing in a financial instrument, the probability model of cash flow is generally unknown. In this paper, we propose a novel framework called the likelihood-free Gaussian process (LFGP), which allows representation of the posterior distributions of interest parameters for scalable problems without directly setting their likelihood functions. The LFGP establishes clusters in which the value of the interest parameter can be considered approximately identical, and it approximates the likelihood of the interest parameter in each cluster to a Gaussian using the asymptotic normality of the maximum likelihood estimator. We expect that the proposed framework will contribute significantly to likelihood-free modeling, particularly by reducing the assumptions for the probability model and the computational costs for scalable problems.
