Machine learningMachine learning
贝叶斯高斯过程
贝叶斯高斯过程（GP）直接在函数上放置概率分布，并使用核函数来编码输入之间的相似性。在观测到数据后，贝叶斯规则将此先验转换为后验，该后验不仅能提供点预测，还能在每个新输入处提供校准的不确定性估计——使其成为机器学习中最具原则性的概率模型之一。
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来源
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 6). Springer. ISBN: 978-0-387-31073-2
如何引用本页
ScholarGate. (2026, June 3). Bayesian Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-gaussian-process
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