Spatial Data and Spatial Statistics

TL;DR: This study employs a Bayesian spatial modelling approach to assess drivers of estuarine debris in four New South Wales estuaries, identifying key predictors of debris abundance, including distance to estuary mouth, access to foreshore, population, and site substrate.

TL;DR: In this article , the authors consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations and introduce parameters to allow temporal adaptivity.

TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.

TL;DR: Stan as discussed by the authors is a probabilistic programming language for specifying statistical models, where a program imperatively defines a log probability function over parameters conditioned on specified data and constants, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration.

TL;DR: This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.