The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore cannot be computed using classical Monte Carlo sampling algorithms. We remedy this by deriving an approximation to the predictively oriented p...
Yann McLatchie, Louis Sharrock, David T. Frazier et al.· 1 citation
Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging. Recent methods such as FlowGP (Moss et al., (2026)) can condition on arbitrary non-linear and non-Gaussian statements, but at considerable cost: an expensive iterative and high-dimensional diffusion that requires hand...
T. Cowperthwaite, Louis Sharrock, L. Astfalck et al.· 0 citations
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the observation model. Suboptimal choices can produce misspecified models that do not capture...
Callum Lau, Jeremias Knoblauch, Louis Sharrock· 0 citations
LatentFlow is introduced, a single framework for conditioning stochastic processes, with no learned neural approximations and no training, that enables conditional sampling in seconds on a single desktop CPU across model classes that have never shared a scalable method.
Louis Sharrock, L. Astfalck, Henry B. Moss· arXiv.org· 0 citations
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