Bayesian networks are rapidly becoming a tool of choice for applied Artificial Intelligence. There have been many medical applications of BNs however few applying data mining methods to epidemiology. In a previous study we looked at such an application to epidemiological data, specifically assessment of risk for coronary heart disease. In that previous study, we featured two Bayesian networks "knowledgeengineered " from the epidemiology literature, but postponed a detailed discussion of their construction. This report provides the full details of our engineering choices, and the reasons for them. It will interest anyone wishing to replicate our results, or check our assumptions or methods. It may also be of interest to others wishing to make a similar Bayesian network from the epidemiological literature for risk prediction of other medical conditions, as it provides a case study in the steps that need to be undertaken. We used the Bayesian network software package Netica to implement the BNs which generated particular challenges. This report notes specific Netica traps and tricks, which may help others avoid some of the di#culties we encountered.

Bayesian networks (BNs) are rapidly becoming a leading tool for applied Artificial Intelligence. Although BNs have been used successfully for many medical diagnosis problems, there have been few applications to epidemiological data where data mining methods play a significant role. In this paper, we look at the application of BNs to epidemiological data, specifically assessment of risk for coronary heart disease (CHD). We build the BNs: (1) by knowledge engineering BNs from two epidemiological models of CHD in the literature; (2) by applying a causal BN learner. We evaluate these BNs using cross-validation. We compared performance in predicting CHD events over 10 years, measuring area under the ROC curve and Bayesian information reward. The knowledge engineered BNs performed as well as logistic regression, while being easier to interpret. These BNs will serve as the baseline in future efforts to extend BN technology to better handle epidemiological data, specifically to model CHD.

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches.

rch is hopeless: dag space is exponential (Robinson, 1977). ffl Taking m(h i je) = P (h i je) as the metric, direct approaches to computing the posterior are also exponential. Reasoning Under Uncertainy Korb 5 Bayesian inference Recall Bayes' Inverse Inference Rule: P (hje) = P (ejh)P (h) P (e) posterior = (likelihood \Theta prior)ff Two distinct learning tasks: Prediction: For engineering, control, etc. To predict, optimal is to use a prediction of x weighted by posterior probabilities P (h i je): P<F11.4

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The development process used by academic researchers often seems unsystematic. A Software Development Life Cycle (SDLC) is seldom considered, commenting is scarce, and external documentation consists of erasure marks left on whiteboards. Configuration management is paid lip-service, but is not standard practice. This paper examines reasons behind the apparent large-scale non-adoption of software engineering in academic research. The effects where it was adopted are examined. Finally, we present an SDLC designed for the academic research environment. 1.

In recent years, electronic tracking has provided voluminous data on vessel movements, leading researchers to try various data mining techniques to find patterns and, especially, deviations from patterns, i.e., for anomaly detection. Here we tackle anomaly detection with Bayesian Networks, learning them from real world Automated Identification System (AIS) data, and from supplementary data, producing both dynamic and static Bayesian network models. We find that the learned networks are quite easy to examine and verify despite incorporating a large number of variables. Combining the mined models improves performance in a variety of cases, demonstrating that learning Bayesian Networks from track data is a promising approach to anomaly detection. 1

BMC Proceedings