1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
Spatial Prediction with Guided Diffusion
Andrew Zammit-Mangion, Lachlan Astfalck, Raphaël Huser +2
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…
Tuning-Free Sampling via Optimization on the Space of Probability Measures
Louis Sharrock, Christopher Nemeth
We introduce adaptive, tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of Wasserstein gradient flows. The result is a suite…
Control Variate-based Stochastic Sampling from the Probability Simplex
Francesco Barile, Christopher Nemeth
This paper presents a control variate-based Markov chain Monte Carlo algorithm for efficient sampling from the probability simplex, with a focus on applications in large-scale Baye…
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
Continuous normalizing flows (CNFs) learn the probability path between a reference distribution and a target distribution by modeling the vector field generating said path using ne…
Spatial Latent Gaussian Modelling with Change of Support
Erick A. Chacón-Montalván, Peter M. Atkinson, Christopher Nemeth +2
Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatia…