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...

Many important marketing issues deal with the study of change in marketing variables based on an analysis of repeated measurements of entities (consumers, salespeople, companies, brands, etc.) observed at different points in time or at different levels of an independent variable. Traditionally, such data have been analyzed using OLS regression pooled across repeated measurements. We describe an...

Bayesian methods are used increasingly across all phases of the pharmaceutical research and development cycle. Examples include early-phase adaptive clinical trial design, population-pk modeling and analysis of safety data in all phases. These applications are driving the need for robust, flexible and novel Bayesian analytics, graphics and reporting software. This article and presentation revie...

Empirical Bayes modeling has a long and celebrated history in statistical theory and applications. After a brief review of the literature, we propose a new dynamic empirical Bayes modeling approach which provides flexible and computationally efficient methods for the analysis and prediction of longitudinal data from many individuals. This dynamic empirical Bayes approach pools the cross-section...

Gaussian process regression has proven very powerful in statistics, machine learning and inverse problems. A crucial aspect of the success this methodology, a wide range applications to complex real-world problems, is hierarchical modeling hyperparameters. The purpose paper study two paradigms parameters: one from probabilistic Bayesian perspective, particular, empirical Bayes approach that bee...

The hybrid Huberized support vector machine (HHSVM) with the elastic-net penalty has been developed for cancer tumor classification based on thousands of gene expression measurements. In this paper, we develop a Bayesian formulation of the hybrid Huberized support vector machine for binary classification. For the coefficients of linear classification boundary, we propose a new type of prior, wh...

Support vector machine (SVM) has been successfully applied for cancer tumor classification based on thousands of gene expression measurements. A modification of SVM known as hybrid Huberized support vector machine (HHSVM) has been developed for the same purpose along with an in built gene selection mechanism with the help of elastic-net penalty. In this paper we develop a Bayesian formulation o...

We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed...