In the context of small area estimation, hierarchical Bayesian (HB) models are often proposed to produce more reliable estimators of small area quantities than direct estimates, such as design-based survey estimators. A method that benchmarks HB estimates with respect to higher level direct estimates and measures the relative inflation in the posterior mean square error of distributions due to ...

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for...

Current methods for hierarchical clustering of data either operate on features of the data or make limiting model assumptions. We present the hierarchy discovery algorithm (HDA), a model-based hierarchical clustering method based on explicit comparison of joint distributions via Bayesian network learning for predefined groups of data. HDA works on both continuous and discrete data and offers a ...

In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specifically, as well as a Bayesian approach to modeling more generally, in tackling these issues. First, they review the Bayesian...

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and o...

There is a growing interest in establishing the relationship between the count data y and numerous covariates x through a generalized linear model (GLM), such as explaining the road crash counts from the geometry and environmental factors. This paper proposes a hierarchical Bayesian method to deal with the negative binomial GLM. The Negative Binomial distribution is preferred for modeling nonne...

In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hi...

We consider a multivariate lognormal frailty model for correlated exchangeable failure time data, where the marginal lifetimes have conditional Weibull distributions. We discuss Bayesian statistical methods to fit this model to experimental data with varying cluster sizes. The Bayesian inferential approach arises naturally from the hierarchical structure of the frailty model. In contrast, imple...

0022-1694/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.jhydrol.2010.09.017 ⇑ Corresponding author. Present address: Departm University of Southern Mississippi, 703 East Beach Dri USA. Tel.: +1 228 818 8855; fax: +1 228 818 8848. E-mail address: [email protected] (W. Wu). Hierarchical Bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into ...