3D extinction mapping using hierarchical Bayesian models

mapping spatial variation of disease using classic and bayesian models

abstract   disease mapping includes a set of statistical techniques that lead to provide clean maps based on estimation of the incidence, prevalence and mortality rates for the users to be able to estimate the distribution of disease reliably. the main aims of disease mapping are to: describe the spatial variation in disease incidence for the formulation of etiological hypotheses; identify area...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models

Calibrating Subjective Probabilities Using Hierarchical Bayesian Models

Abstract. A body of psychological research has examined the correspondence between a judge’s subjective probability of an event’s outcome and the event’s actual outcome. The research generally shows that subjective probabilities are noisy and do not match the “true” probabilities. However, subjective probabilities are still useful for forecasting purposes if they bear some relationship to true ...

Hierarchical Bayesian Models

Those of us who study human cognition have no easy task. We try to understand how people functionally represent and processes information in performing cognitive activities such as vision, perception, memory, language, and decision making. Fortunately, experimental psychology has a rich theoretical tradition, and there is no shortage of insightful theoretical proposals. Also, it has a rich expe...

Mapping Brucellosis Increases Relative to Elk Density Using Hierarchical Bayesian Models

The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus) in northwestern Wyoming. Managemen...