We develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs [1]. We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space t...

Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian belief network is a process to identify and order a set of composite hypotheses, His, of which the posterior probabilities are the I largest; i.e., Pr(Hii&) 2 Pr(H21&) 2 . . . L Pr(&ISlz)~ A composite hypothesis is defined as an instantiation of all the non-evidence variables in the network. It could be shown...

Multi-Entity Bayesian Network (MEBN) is an expressive first-order probabilistic logic that represents the domain using parameterized fragments of Bayesian networks. Probabilistic-OWL (PR-OWL) uses MEBN to add uncertainty support to OWL, the main language of the Semantic Web. The reasoning in MEBN is made by the construction of a Situation-Specific Bayesian Network (SSBN), a minimal Bayesian net...

In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and...

We present Incremental Thin Junction Trees, a general framework for approximate inference in static and dynamic Bayesian Networks. This framework incrementally builds junction trees representing probability distributions over a dynamically changing set of variables. Variables and their conditional probability tables can be introduced into the junction tree Υ, they can be summed out of Υ and Υ c...

This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution sy...

In this paper we propose a novel method to solve a kidnapped robot localization problem. A mobile robot plans its sensing action for localization using learned Bayesian network’s inference. Concretely, we represent the contextual relation between the local sensing results, actions and the global localization beliefs using the Bayesian network. The Bayesian network structure is learned from comp...

In this paper, we propose a learning algorithm which is non-local in the sense that subsequent Bayesian network structures might differ from each other by more than one edge in the learning process. This is achieved by optimizing the orientations of all edges in the graph simultaneously in a heuristic way. As a benefit, the algorithm is quite robust against problems entailed by Markov equivalen...

Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence relations that cannot be captured in a Bayesian network structure, and can sometimes provide computationally more efficient representations than Bayesian networks. In this paper we present an algorithm for learning PD...

Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection-mutation model has unobservable fitness parameters and, to estimate them, we use an Approximate Bayesian Computation (ABC) alg...