uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....

Computer model calibration involves combining information from simulations of a complex computer model with physical observations of the process being simulated by the model. Increasingly, computer model output is in the form of multiple spatial fields, particularly in climate science. We study a simple and effective approach for computer model calibration with multivariate spatial data. We dem...

The cougar is a large predatory feline found widely in the Americas. To assess genetic differentiation in cougars, previous research used a Bayesian clustering model with genetic data and found evidence of genetic population structure in cougars, with suggestive but indistinct spatial boundaries between subpopulations. These patterns are intriguing, particularly because the model ignored spatia...

In this technical report, we review the Bayesian melding model, proposed by Fuentes and Raftery (2005). We give technical details about using Gibbs sampling algorithm to fit the model and critically assess the model through various simulation studies and applications of combing ozone measurements with simulated ozone levels from deterministic model AQM. keywords: Bayesian melding, Bayesian hier...

BACKGROUND Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia....

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were p...

This paper applies a Bayesian network to model multi criteria distribution maps and to discover knowledge contained in spatial data. The procedure consists of three steps: pre processing map data, training the Bayesian Network model using distribution maps of Australia and testing the generalization and diagnosis of the model using individual states’ maps. The Bayesian network that we used in t...

animal models are used to model the observations of animal performance that are genetically dependent.these models are considered as generalized linear mixed models and the genetic correlation structure of data is considered through random effects of breeding values. one goal of the mentioned models is to estimate variance components. in this research, an approximate bayesian approach presented...

The degree of segregation between two or more sub-populations has been studied since the 1950s, and examples include segregation along racial and religious lines. The Dissimilarity index is a commonly used measure to numerically quantify segregation, using population level data for a set of areal units that comprise a city or country. However, the construction of this index usually ignores the ...