BACKGROUND One of the methods used in the analysis of data related to diseases, and their underlying reasons is drawing geographical map. Mapping diseases is a valuable tool to determine the regions of high rate of infliction requiring therapeutic interventions. The objective of this study was to investigate obesity pattern in Iran by drawing geographical maps based on Bayesian spatial model to...

Spatial environmental data sometimes include below detection limit observations (i.e. censored values reported as less than a level of detection). Historically, the most common practice for analysis of such data has been to replace the censored observations with some function of the level of detection (LOD), like LOD/2. We show that estimates and standard errors found using this single substitu...

Abstract: Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classifica...

Article history: Received 19 March 2008 Received in revised form 8 August 2008 Accepted 5 January 2009 Available online 13 January 2009

Introduction The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data w...

Several standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above methods do not consider all the available information (they consider special independency assumptions to s...

We introduce a new technique to select the number of components of a mixture model with spatial dependence. It consists in an estimation of the Integrated Completed Likelihood based on a Laplace’s approximation and a new technique to deal with the normalizing constant intractability of the hidden Potts model. Our proposal is applied to a real satellite image.

Objective priors, especially reference priors, have been well-known for geostatistical data since Berger, et al. (2001). A long-standing problem is to develop objective Bayes inference for spatially correlated data with nugget effects. In this paper, objective priors for such cases are systematically studied. In addition to the Jeffreys priors and commonly used reference priors, two types of “e...

We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel....