In this paper we present a hierarchical model of linear regression functions in the context of multi–task learning. The parameters of the linear model are coupled by a Dirichlet Process (DP) prior, which implies a clustering of related functions for different tasks. To make approximate Bayesian inference under this model we apply the Bayesian Hierarchical Clustering (BHC) algorithm. The experim...

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical d...

We consider stochastic weather models originally developed for rainfall simulations to build a hierarchical Bayesian mixture model daily forecasts using endogenous and external information. as seasonal-varying of Bernoulli distribution occurrence gamma the amount. The scheme allows inclusion predictors reduce bias variance forecasts, while framework promotes better understanding reduction in pa...