8 citations · 11 across the 10 of their papers we have counts for
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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…
Penalized Maximum Likelihood Estimator for Mixture of von Mises-Fisher Distributions
Tin Lok James Ng
The von Mises-Fisher distribution is one of the most widely used probability distributions to describe directional data. Finite mixtures of von Mises-Fisher distributions have foun…
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…
Deep Compositional Spatial Models
Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu +1
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes ma…
Model-based clustering for random hypergraphs
Tin Lok James Ng, Thomas Brendan Murphy
A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension…
Multiresolution network models
Bailey K. Fosdick, Tyler H. McCormick, Thomas Brendan Murphy +2
Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of…