A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is, individuals located at similar points in space may tend to exhibit similar choice behavior. The...

Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and dependencies simultaneously for The parameters are calculated using Bayesian inference based Markov chain Monte Carlo sampling. DMSOP model performs effectively with simulated data. Furthermore, applied two...

We introduce a new class of nonstationary covariance functions for spatial modelling. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location. The class includes a nonstationary version of the Matérn stationary covariance, in which the differentiability of the spatial surface is controlled by a parameter, freeing one from fixing th...

In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies...

The goal of this paper is to establish a scientifically founded methodology for forensic automatic speaker recognition. The interpretation of recorded speech as evidence in the forensic context presents particular challenges. The means proposed in the paper for dealing with them is through Bayesian inference. This leads to the formulation of a likelihood ratio measure of evidence which weighs t...

Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for ‘default’ priors in the literature, often a conditionally conjugate inverse-gamma specification is used, despite documented drawbacks with thi...

Natural language acquisition relies on appropriate generalization: the ability to produce novel sentences, while learning to restrict productions to acceptable forms in the language. Psycholinguists have proposed various properties that might play a role in guiding appropriate generalizations, looking at learning of verb alternations as a testbed. Several computational cognitive models have exp...

Two types of spatial regression models, a spatial lag model (SLM) and a spatial error model (SEM), were applied to fit the height–diameter relationship of trees. SEM had better model fitting and performance than both SLM and ordinary least squares. Moran’s I coefficients showed that SEM effectively reduced the spatial autocorrelation in the model residuals. Both real data and Monte Carlo simula...