3 papers
stat.CO2020
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…
stat.AP2019
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…
stat.AP2018
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…