Improvement of the Fine tuning algorithm

Khalil El Hindi has developed a fine-tuning algorithm toimprove the classification accuracy of the Naive Bayes. His algorithm optimizes the conditional probability tables of the Naive Bayes after thetraining phase. The values of the probabilities of a variable are modified if it causes misclassification of a training instance. The algorithm out-performs in many cases the Naive Bayes. We analyze the performanceof the algorithm, discussed its issues, and compare it to a modified algorithm. The new algorithm simplifies the formula used in the fine-tuning algorithm and uses a more efficient scoring metric, the Brier score, tofine-tune the probabilities. The new algorithm shows an improvement in terms of classification accuracy on benchmark data sets compared to the Naive Bayes and fine tuned Naive Bayes

Data driven Bayesian network to predict critical alarm

Modern industrial plants rely on alarm systems to ensure their safe and effective functioning. Alarms give the operator knowledge about the current state of the industrial plants. Trip alarms indicating a trip event indicate the shutdown of systems. Trip events in power plants can be costly and critical for the running of the operation.This paper demonstrates how trips events based on an alarm log from an offshore gas production can be reliably predicted using a Bayesian network. If a trip event is reliably predicted and the main cause of it is identified, it will allow the operator to prevent it. The Bayesian network model developed to predict trip events is purely data-driven and relies only on historic data from the alarms log from offshore gas production. We describe the method used to build the Bayesian network and the approach used to identify the most key alarm related to the Trip. We then assess theperformance of the Bayesian network on the alarm log of an offshore gas production. The preliminary performance results show significant potential in predicting trips and identifying key alarms. The model is developed to support decision-making of a human operator and increase the performance of the plant

MPE inference in conditional linear gaussian networks

Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is the problem of nding a con guration of the remaining variables with maximum posterior probability. This problem has previously been addressed for discrete Bayesian networks and can be solved using inference methods similar to those used for finding posterior probabilities. However, when dealing with hybrid Bayesian networks, such as conditional linear Gaussian (CLG) networks, the MPE problem has only received little attention. In this paper, we provide insights into the general problem of fi nding an MPE con guration in a CLG network. For solving this problem, we devise an algorithm based on bucket elimination and with the same computational complexity as that of calculating posterior marginals in a CLG network. We illustrate the workings of the algorithm using a detailed numerical example, and discuss possible extensions of the algorithm for handling the more general problem of fi nding a maximum a posteriori hypothesis (MAP)

A Review of Inference Algorithms for Hybrid Bayesian Networks

Hybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which extends the applicability of the Bayesian network framework in general. However, this extra feature also comes at a cost: inference in these types of models is computationally more challenging and the underlying models and updating procedures may not even support closed-form solutions. In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks. The methods covered in the paper are organized and discussed according to their methodological basis. We consider how the methods have been extended and adapted to also include (hybrid) dynamic Bayesian networks, and we end with an overview of established software systems supporting inference in these types of models

MAP inference in dynamic hybrid Bayesian networks

In this paper, we study the maximum a posteriori (MAP) problem in dynamic hybrid Bayesian networks. We are interested in finding the sequence of values of a class variable that maximizes the posterior probability given evidence. We propose an approximate solution based on transforming the MAP problem into a simpler belief update problem. The proposed solution constructs a set of auxiliary networks by grouping consecutive instantiations of the variable of interest, thus capturing some of the potential temporal dependences between these variables while ignoring others. Belief update is carried out independently in the auxiliary models, after which the results are combined, producing a configuration of values for the class variable along the entire time sequence. Experiments have been carried out to analyze the behavior of the approach. The algorithm has been implemented using Java 8 streams, and its scalability has been evaluated