1 citations · 2 across the 5 of their papers we have counts for
7 papers
Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Bokgyeong Kang, Andrew Zammit-Mangion, Erin M. Schliep +1
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computati…
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…
Conditioning Gaussian Processes on Almost Anything
Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite +5
Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalenc…
Bayesian Sphere-on-Sphere Regression with Optimal Transport Maps
Tin Lok James Ng, Kwok-Kun Kwong, Jiakun Liu +1
Spherical regression, in which both covariates and responses lie on the sphere, arises in many scientific applications and has attracted considerable methodological attention in re…
Mixture Modeling with Normalizing Flows for Spherical Density Estimation
Tin Lok James Ng, Andrew Zammit-Mangion
Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years. Many flexible families of norma…
Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport
Tin Lok James Ng, Andrew Zammit-Mangion
Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistic…
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport
Tin Lok James Ng, Andrew Zammit-Mangion
Non-homogeneous Poisson processes are used in a wide range of scientific disciplines, ranging from the environmental sciences to the health sciences. Often, the central object of i…