Humans are exposed to a diverse mixture of chemical and non-chemical exposures across
their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure …

The yearly cycles in vegetation greenness are among the most important drivers of
ecosystem processes. Predictive models for the timing of vegetation greenup and …

Quantifying spatial and/or temporal associations in multivariate geolocated data of different
types is achievable via spatial random effects in a Bayesian hierarchical model, but severe …

A two-stage hierarchical Bayesian model is developed and implemented to estimate forest
biomass density and total given sparsely sampled LiDAR and georeferenced forest …

As the spatial features of multivariate data are increasingly central in researchers' applied
problems, there is a growing demand for novel spatially-aware methods that are flexible …

Massive geolocated datasets are increasingly common in many scientific fields and industry.
While latent Gaussian Process (GP) models are frequently chosen to perform statistical …