Bayesian Hierarchical Random Effects Models in Forensic Science

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

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Bayesian Determination of Sample Size in Longitudinal Studies with Binary Responses Using Random Effects Models

Sample size determination is important in all statistical studies including longitudinal studies. This is usually done by considering a target power to reduce the costs of sampling. Choosing the right sample size using efficient methods, ensures that the researcher achieve goal of the study, by spending the least amount of energy, time and money. In this article, using a method based on simulat...

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

Approximate Bayesian Inference for Hierarchical Gaussian Markov Random Fields Models

Many commonly used models in statistics can be formulated as (Bayesian) hierarchical Gaussian Markov random field models. These are characterised by assuming a (often large) Gaussian Markov random field (GMRF) as the second stage in the hierarchical structure and a few hyperparameters at the third stage. Markov chain Monte Carlo is the common approach for Bayesian inference in such models. The ...

Analysis of Hierarchical Bayesian Models for Large Space Time Data of the Housing Prices in Tehran

Housing price data is correlated to their location in different neighborhoods and their correlation is type of spatial (location). The price of housing is varius in different months, so they also have a time correlation. Spatio-temporal models are used to analyze this type of the data. An important purpose of reviewing this type of the data is to fit a suitable model for the spatial-temporal an...