DISSECTING MAGNETAR VARIABILITY WITH BAYESIAN HIERARCHICAL MODELS

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

on some bayesian statistical models in actuarial science with emphasis on claim count

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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...

Hierarchical Bayesian Nonparametric Models with Applications∗

Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that parameters are endowed with distributions which may themselves introduce new parameters, and this construction recurses. A common motif in hierarchical modeling is that of the conditionally independent hierarchy, in which a set of parameters are coupled by making their distributions depend on a shared ...

Optimizing Prediction with Hierarchical Models: Bayesian Clustering

A frequent statistical problem is that of predicting a set of quantities given the values of some covariates, and the information provided by a training sample. These prediction problems are often structured with hierarchical models that make use of the similarities existing within classes of the population. Hierarchical models are typically based on a ‘natural’ definition of the clustering whi...