Machine learningMachine learning
Bayesian Gaussian Process
A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.
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Sources
- 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
Related methods
Referenced by
Active learning Gaussian processBayesian Metric LearningBayesian one-class SVMBayesian Online LearningBayesian Transfer LearningEnsemble Gaussian ProcessExplainable Gaussian ProcessGaussian ProcessRegularized Gaussian ProcessRobust Gaussian ProcessSelf-supervised Gaussian ProcessSemi-supervised Gaussian Process