Statistical models for the occurrence of extreme or rare events has been applied in a variety of areas, but little work has been done in extending these ideas to spatial data sets. Here, a brief introduction to the theory of extreme-value statistics is given, and a hierarchical Bayesian framework is used to incorporate a spatial component into the model. Two examples are used to motivate the ap...

In current computed tomography (CT) examinations, the associated X-ray radiation dose is of significant concern to patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) parameter as low as reasonably achievable in data...

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population co...

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such...

We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of audiovisual information. The observer adopts the maximum a posteriori approach to estimate the physically delivered position or timing of presented stimuli, simultaneously judging whether they are from the same source or not. Severa...

This paper considers the panel probit model with spatial dependency from a Bayesian point of view. We consider Markov chain Monte Carlo methods to estimate the parameters of the model. Our approach is illustrated with simulated data set. Furthermore, we explore the spatial interaction of business cycle across 47 prefectures from the period 1991 to 2000 in Japan. Spatial dependency can be found ...

This paper proposes a four-pronged approach to efficient Bayesian estimation and prediction for complex Bayesian hierarchical Gaussian models for spatial and spatiotemporal data. The method involves reparameterizing the variance/covariance structure of the model, reformulating the means structure, marginalizing the joint posterior distribution, and applying a simplex-based slice sampling algori...

We consider the problem of multiband image clustering and segmentation. We propose a new methodology for doing this, called modelbased cluster trees. This is grounded in model-based clustering, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, approximated using BIC...

State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian n...