The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification models even when there are clear conditional dependencies. The SBC can serve as an excellent tool for initial exploratory data analysis when coupled with a visualizer that makes its structure comprehensible. We describe such a visual representation of the SBC model that has been successfully implemented. We describe the requirements we had for such a visualization and the design decisions we made to satisfy them. Keywords:Classification, simple/naive-Bayes, visualization.

The digital personal calendar has long been established as an effective tool for supporting workgroup coordination. For the new class of ubiquitous computing applications, however, the calendar can also be seen as a sensor, providing both location and availability information to these applications. In most cases, however, the calendar represents a sequence of events that people could (or should) attend, not their actual daily activities. To assist in the accurate determination of user whereabouts and availability, we present Ambush, a calendar system extension that uses a Bayesian model to predict the likelihood of one’s attendance at the events listed on his or her schedule. We also present several techniques for the visual display of these likelihoods in a manner intended to be quickly interpreted by users examining the calendar.

cc.gatech.edu In this paper, we describe Augur, a groupware calendar system to support personal calendaring practices, informal workplace communication, and the socio-technical evolution of the calendar system within a workgroup. Successful design and deployment of groupware calendar systems have been shown to depend on several converging, interacting perspectives. We describe calendar-based work practices as viewed from these perspectives, and present the Augur system in support of them. Augur allows users to retain the flexibility of personal calendars by anticipating and compensating for inaccurate calendar entries and idiosyncratic event names. We employ predictive user models of event attendance, intelligent processing of calendar text, and discovery of shared events to drive novel calendar visualizations that facilitate interpersonal communication. In addition, we visualize calendar access to support privacy management and long-term evolution of the calendar system.

One of the key factors for the acceptance of expert systems in real world domains is the capability to explain their reasoning. This paper describes the basic properties that characterize explanation methods and reviews the methods developed up to date for explanation in Bayesian networks.

In this paper we illustrate a simple scheme for dividing a complex Bayes network into a system model and a collection of smaller evidence models. While the system model maintains a permanent record of the state of the system of interest, the evidence models are only used momentarily to absorb evidence from specific observations or findings and then discarded. This paper describes an implementation of a system model–evidence model complex in which each system and evidence model has a separate Bayes net and Markov tree representation. As necessary, information is propagated between common Markov tree nodes of the evidence and system models. While mathematically equivalent to the full Bayes network, the system model–evidence model complex allows us to (a) separate the seldom used evidence model portions from the core system model thus reducing search and propagation time in the network and (b) easily replace the evidence models (this is particular advantageous in educational examples in which new test items are often introduced to prevent overexposure of assessment tasks). 1 System Models and Evidence Models Putting all possible observable variables of a large, complex model into a computational system is often impractical. One approach to such large problems is to decompose a Bayesian network into many smaller model fragments which would be assembled into the full model as needed. This approach is