Abstract Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon applied work. This is partly due to a lack accessible, flexible software for estimation models. Established probabilistic struggles with specifics econometrics, while classical implementations do not harness flexibility modelling. In this paper, I present layered, objected...

This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The sp...

We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic,” and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increased power to detect clusters and (since randomization testing is unnecessary) much faster runtime. We ...

BACKGROUND Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial...

the objective of the present study was to estimate heritability values for some performance and egg quality traits of native fowl in isfahan breeding center using reml and bayesian approaches. the records were about 51521 and 975 for performance and egg quality traits, respectively. at the first step, variance components were estimated for body weight at hatch (bw0), body weight at 8 weeks of a...

Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has b...

A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate th...

This paper presents a fully Bayesianway to solve the simultaneous localization and spatial prediction problemusing aGaussianMarkov randomfield (GMRF)model. The objective is to simultaneously localize robotic sensors and predict a spatial field of interest using sequentially collected noisy observations by robotic sensors. The set of observations consists of the observed noisy positions of robot...