17 citations · 23 across the 5 of their papers we have counts for
6 papers
Profile likelihoods for parameters in Gaussian geostatistical models
Ruoyong Xu, Patrick Brown
Profile likelihoods are rarely used in geostatistical models due to the computational burden imposed by repeated decompositions of large variance matrices. Accounting for uncertain…
Poisson Cluster Process Models for Detecting Ultra-Diffuse Galaxies
Dayi Li, Alex Stringer, Patrick E. Brown +2
We propose a novel set of Poisson Cluster Process (PCP) models to detect Ultra-Diffuse Galaxies (UDGs), a class of extremely faint, enigmatic galaxies of substantial interest in mo…
Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models
Alex Stringer, Patrick Brown, Jamie Stafford
We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the add…
Fully Bayesian inference for spatiotemporal data with the multi-resolution approximation
Luc Villandré, Jean-François Plante, Thierry Duchesne +1
Large spatiotemporal datasets are a challenge for conventional Bayesian models because of the cubic computational complexity of the algorithms for obtaining the Cholesky decomposit…
Geographically-dependent individual-level models for infectious diseases transmission
Md Mahsin, Rob Deardon, Patrick Brown
Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling…
Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC
Reihaneh Entezari, Patrick E. Brown, Jeffrey S. Rosenthal
In this paper, we perform Bayesian Inference to analyze spatial tree count data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian Generalize…