L. Astfalck

Spatial Prediction with Guided Diffusion

Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data. It is commonly carried out using hierarchical models, which comprise a process model describing the latent process of interest, and a data model describing how the process is observed. The tar...

Identifying the Antarctic melt contribution to Last Interglacial sea level from Eurasian relative sea-level records

Feature Space Adaptation for Effortless Gaussian Process Flows

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...

LatentFlow: A General Framework for Conditioning Stochastic Processes

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.