Variational Tobit Gaussian Process Regression
Variational Tobit Gaussian Process Regression
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DOI：
10.1007/s11222-023-10225-3
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发表时间：
2023-03
影响因子：
2.2
通讯作者：
M. Basson;Tobias M. Louw;Theresa R. Smith
中科院分区：
文献类型：
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作者：
M. Basson;Tobias M. Louw;Theresa R. Smith
我们提出了一种基于变分推理的框架，用于训练受审查观测数据影响的高斯过程回归模型。数据审查是数据收集过程中遇到的典型问题，并且需要专门的技术来执行推理，因为所得的概率模型通常在分析上难以处理。在本文中，我们利用变分稀疏高斯过程诱导变量框架和局部变分方法来计算概率模型的真实对数边际似然的可分析处理的下界，该下界可用于执行贝叶斯模型训练和推理。我们在合成产生的、受噪声破坏的观测数据以及经过人工审查的真实世界数据集上展示了所提出的框架。由此产生的预测与现有的数据审查方法相当，但显着降低了计算成本。
We propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically analytically intractable. In this article we exploit the variational sparse Gaussian process inducing variable framework and local variational methods to compute an analytically tractable lower bound on the true log marginal likelihood of the probabilistic model which can be used to perform Bayesian model training and inference. We demonstrate the proposed framework on synthetically-produced, noise-corrupted observational data, as well as on a real-world data set, subject to artificial censoring. The resulting predictions are comparable to existing methods to account for data censoring, but provides a significant reduction in computational cost.