We describe an approach to incorporating Bayesian priors in the MAXQ framework for hierarchical reinforcement learning (HRL). We define priors on the primitive environment model and on task pseudo-rewards. Since models for composite tasks can be complex, we use a mixed model-based/model-free learning approach to find an optimal hierarchical policy. We show empirically that (i) our approach resu...

We present a new hierarchical Bayesian model for unsupervised topic segmentation. This new model integrates a point-wise boundary sampling algorithm used in Bayesian segmentation into a structured topic model that can capture a simple hierarchical topic structure latent in documents. We develop an MCMC inference algorithm to split/merge segment(s). Experimental results show that our model outpe...

Model validation involves quantitatively comparing model predictions with experimental observations, both of which contain uncertainty. A building block approach to model validation may proceed through various levels, such as material to component to subsystem to system. This paper presents a structural equation modeling-based Bayesian approach to make use of the low-level data for system-level...

Hierarchical learning machines such as layered perceptrons, radial basis functions, Gaussian mixtures are non-identifiable learning machines, whose Fisher information matrices are not positive definite. This fact shows that conventional statistical asymptotic theory cannot be applied to neural network learning theory, for example either the Bayesian a posteriori probability distribution does no...

Two major approaches have developed within Bayesian statistics to address uncertainty in the prior distribution and in the overall model more generally. First, methods of model checking, including those assessing prior-data conflict, determine whether the prior and the rest of the model are adequate for purposes of inference and estimation or other decision-making. The main drawback of this app...

The use of a Bayesian Hierarchical Model (BHM) to interpret breath measurements obtained during a C Octanoic Breath Test (COBT) is demonstrated. The statistical analysis was implemented using WinBUGS, a commercially available computer package for Bayesian inference. A hierarchical setting was adopted where poorly defined parameters associated with a delayed Gastric Emptying (GE) were able to "b...