Hierarchical Graphical Bayesian Models in Psychology

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

Cuts in Bayesian graphical models

The cut function defined by theOpenBUGS software is described as a “valve” that prevents feedback in Bayesian graphical models. It is shown that theMCMC algorithm applied by OpenBUGS in the presence of a cut function does not converge to a well-defined limiting distribution. However, it may be improved by using tempered transitions. The cut algorithm is compared with multiple imputation as a go...

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...

Bayesian Estimation in Hierarchical Models

Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn ...

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...