Gaussian process

Statistical model

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Gaussian Processes (3) · uncertainty quantification (1) · autonomous underwater vehicles (1) · environmental reconstruction (1) · deep learning (1) · informative path planning (1) · aquatic monitoring (1) · Bayesian Neural Networks (1) · Statistical Convergence (1) · Machine Learning Theory (1) · Scalable Inference (1) · Identifiability (1) · Covariance Functions (1) · Nyström Approximation (1) · medical image foundation models (1) · synthetic data (1) · RaSD framework (1) · medical AI (1) · data scarcity (1) · diagnostic tools (1)

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In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution. The distribution of a Gaussian process is the joint distribution of all those (infinitely many) random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space.

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