
Kernel packets (KPs) provide a general framework to construct compactly supported basis functions for Gaussian processes (GPs) driven by stochastic differential equations (SDEs), enabling efficient training and prediction of GP models.


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a-general-theory-for-constructing-compactly-supported-basis-functions-for-gaussian-processes-driven-by-stochastic-differential-equations


A General Theory for Constructing Compactly Supported Basis Functions for Gaussian Processes Driven by Stochastic Differential Equations


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This paper proposes novel Gaussian process models for vector-valued signals on manifolds that are intrinsically defined and account for the geometry of the space, addressing limitations of previous extrinsic approaches.


intrinsic-gaussian-vector-fields-modeling-tangential-vector-signals-on-manifolds


Intrinsic Gaussian Vector Fields: Modeling Tangential Vector Signals on Manifolds
