Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we u...

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-tria...

The contribution of this paper is the adaption of data driven methods for decomposition of tangent shape variability proposed in a probabilistic framework. By Bayesian model selection we compare two generative model representations derived by principal components analysis and by maximum autocorrelation factors analysis.

An efficient Bayesian hierarchical model for spatial extremes on a large domain is proposed. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression. Using a composite likelihood approach and a method for incorporating stations with missing data, we are able...