... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling

The mixture-of-experts model is a static neural network architecture in that it learns input-output mappings where the output is directly influenced by the current input but not previous inputs. We explore a dynamic version of the mixture-of-experts model by introducing feedback

We describe a new kind of user model and a new kind of expert model and show how these models can be used to individualize the selection of instructional topics.

In this paper we propose a modelling technique designed to combine the results of different experts (forecasting techniques, in our case) where each expert model (called Local Expert) is developed using only part of the data set. Many expert models are developed for the same part of the data set

It is possible to combine multiple probabilistic models of the same data by multiplying the probabilities together and then renormalizing. This is a very efficient way to model high-dimensional data which simultaneously satisfies many different low-dimensional constraints. Each individual expert

Abstract. The statistical properties of the likelihood ratio test statistic (LRTS) for mixture-of-expert models are addressed in this paper. This question is essential when estimating the number of experts in the model. Our purpose is to extend the existing results for mixtures (Liu and Shao, 2003

This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transition analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams

. We describe a new kind of user model and a new kind of expert model and show how these models can be used to individualize the selection of instructional topics. The new user model is based on observing the individual's behavior in a natural environment over a long period of time, while

the additional loss of the algorithm over the sum of the losses of the best experts for each segment. This is to model situations in which the examples change and different experts are best for certain segments of the sequence of examples. In the single segment case, the additional loss is proportional to log n

Abstract. One aim of the Generalized Intelligent Framework for Tutoring (GIFT) is to reduce the time and cost of authoring Intelligent Tutoring Systems. Recent work with SimStudent offers a promising approach to the efficient au-thoring of expert models and misconception libraries. SimStudent works