Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes Page 1
Bayesian Analysis (2017) 12, Number 4, pp. 939–967 Joint Species Distribution Modeling …

Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-
Gaussian spatial data is computationally intensive. The computational challenge is due to …

We present a novel data set for drought in the continental US (CONUS) built to enable
computationally efficient spatio-temporal statistical and probabilistic models of drought. We …

We propose a Bayesian stochastic cellular automata modeling approach to model the
spread of wildfires with uncertainty quantification. The model considers a dynamic …

Spatial voting models of legislators' preferences are used in political science to test theories
about their voting behavior. These models posit that legislators' ideologies as well as the …

Gaussian random field (GRF) models are widely used in spatial statistics to capture spatially
correlated error. Gaussian processes can easily be replaced by the less commonly used …

The current emergence of deep learning has enabled state-of-the-art approaches to achieve
a major breakthrough in various fields such as object detection. However, the popular object …

We develop a novel Bayesian method to select important predictors in regression models
with multiple responses of diverse types. A sparse Gaussian copula regression model is …

Current approaches for modeling discrete-valued outcomes associated with spatially-
dependent areal units incur computational and theoretical challenges, especially in the …

Collective efficacy—the capacity of communities to exert social control toward the realization
of their shared goals—is a foundational concept in the urban sociology and neighborhood …