While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of com...

The goal of this paper is to describe and demonstrate the applicationof Bayesian networks in a generic automatic video surveillance system. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed and noise is suppressed by statisticalmethods. The probability that a scenario occurs is then computed from these mobile object prop...

This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and ...

Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC) system. Based on the model of PEMFC, fault...

Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is less clear if the teacher distribution is unknown. I define a class of averaging procedures, the temp...

Flight delay creates major problems in the current aviation system. Methods are needed to analyze the manner in which micro-level causes propagate to create system-level patterns of delay. Traditional statistical methods are inadequate to the task. This paper proposes the use of Bayesian networks (BNs) to investigate and visualize propagation of delays among airports. The BN structure was devel...

Segmentation and tracking of objects in video sequences is important for a number of applications. In the supervised variant, segmentation can be achieved by modelling the probability density of image observations taken from an object for use in a Bayesian classifier, and Gaussian mixture models have been applied to this task by several researchers. Motivated by practical difficulties we have e...

A challenge in dialogue act recognition is the mapping from noisy user inputs to dialogue acts. In this paper we describe an approach for re-ranking dialogue act hypotheses based on Bayesian classifiers that incorporate dialogue history and Automatic Speech Recognition (ASR) N-best information. We report results based on the Let’s Go dialogue corpora that show (1) that including ASR N-best info...

The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same...

One-dimensional Bayesian network classifiers (OBCs) are popular tools for classification [2]. An OBC is a Bayesian network [4] consisting of just a single class variable and several feature variables. Multi-dimensional Bayesian network classifiers (MBCs) were introduced to generalise OBCs to multiple class variables [1, 6]. Classification performance of OBCs is known to be rather good. Experime...