We investigate properties of Bayesian networks (BNs) in the context of robust state estimation. We focus on problems where state estimation can be viewed as a classification of the possible states, which in turn is based on the fusion of heterogeneous and noisy information. We introduce a coarse perspective of the inference processes and show that classification with BNs can be very robust, eve...

This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent processes that include several distinct phases. In essence, a GBN is a model that combines several Bayesian networks (BNs) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to ...

Bayesian networks have gained increasing attention in recent years. One key issue in Bayesian networks (BNs) is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. Under these circumstances, the learning algorithms are required to operate in a high-dimensional search space...

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 “...

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTB...

Search and score techniques have been widely applied to the problem of learning Bayesian Networks (BNs) from data. Many implementations focus on finding an ordering of variables from which edges can be inferred. Although varying across data, most search spaces for such tasks exhibit many optima and plateaus. Such characteristics represent a trap for population-based algorithms as the diversity ...

Bayesian networks (BN) are an extensively used graphical model for representing a probability distribution in artificial intelligence, data mining, and machine learning. In this paper, we propose a simple model for large random BNs with bounded indegree, that is, large directed acyclic graphs (DAG) where the edges appear at random and each node has at most a given number of parents. Using this ...

Bayesian networks (BNs) are advantageous when representing single independence models, however they do not allow us to model changes among the relationships of the random variables over time. Due to such regime changes, it may be necessary to use di↵erent BNs at di↵erent times in order to have an appropriate model over the random variables. In this paper we propose two extensions to the traditi...

Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS’s) and Bayesian networks (BNs). FRS’s provide an excellent representation for the organizational structure of large complex domains, but their applicability is limited because of their inability to deal with uncertainty and noise. BNs provide an intuitive and coherent probabil...

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the nee...