In this paper we present an integrated theoretical approach for student modelling based on an Adaptive Bayesian Network. A mathematical formalization of the Adaptive Bayesian Network is provided, and new question selection criteria presented. Using this theoretical framework, a tool to assist in the diagnosis process has been implemented. This tool allows the definition of Bayesian Adaptive Tes...

Bayesian inference for complex hierarchical models with smoothing splines is typically intractable, requiring approximate inference methods for use in practice. Markov Chain Monte Carlo (MCMC) is the standard method for generating samples from the posterior distribution. However, for large or complex models, MCMC can be computationally intensive, or even infeasible. Mean Field Variational Bayes...

Abstract This paper demonstrates the utility of multilevel Bayesian models of data annotation for classifiers (also known as coding or rating). The observable data is the set of categorizations of items by annotators (also known as raters or coders) from which data may be missing at random or may be replicated (that is, it handles fixed panel and varying panel designs). Estimated model paramete...

BENJAMIN R. SAVILLE: Bayesian Multilevel Models and Medical Applications. (Under the direction of Dr. Amy Herring.) Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. We propose ...

School Effects and Labor Market Outcomes for Young Adults in the 1980s and 1990s This study examines high school effects on the labor market success of young adults, above and beyond individual and family characteristics. We employ data from two longitudinal, nationally probability samples: the National Longitudinal Study and the High School and Beyond study and the 5th and 4th follow-up studie...

Bayesian networks have recently found many applications in systems reliability; however, the focus has been on binary outcomes. In this paper we extend their use to multilevel discrete data and discuss how to make joint inference about all of the nodes in the network. These methods are applicable when system structures are too complex to be represented by fault trees. The methods are illustrate...