Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification ac...

A Bayesian network is a graphical model for representing probabilistic relationships among a set of variables. It is an important model for business analysis. Bayesian network learning methods have been applied to business analysis where data privacy is not considered. However, how to learn a Bayesian network over private data presents a much greater challenge. In this paper, we develop an appr...

We present an efficient Bayesian algorithm for identifying the shape of object from noisy far field data. The data is obtained by illuminating with one or more incident waves. Bayes' theorem provides a framework to find posterior distribution parameters that determine scatterer. compute using Markov Chain Monte Carlo (MCMC) method Gibbs sampler. principal novelty this work replace forward far-f...

Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from mi...

This paper incorporates Belief Propagation into an instance of Estimation of Distribution Algorithms called Estimation of Bayesian Networks Algorithm. Estimation of Bayesian Networks Algorithm learns a Bayesian network at each step. The objective of the proposed variation is to increase the search capabilities by extracting information of the, computationally costly to learn, Bayesian network. ...

Bayesian network has been used extensively in data mining. The Privacy-Preserving data mining algorithm based on quantum ant colony optimization is proposed in this paper. The algorithm is based on distributed database. The algorithm is divided into two steps. In the first step, the modified quantum ant colony optimization algorithm is used to get the local Bayesian network structure. The purpo...

Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks. We first encode an intervention as changing a conditional probabilistic table into a partial intervention table. We next introduce a jointree algorithm to compu...

We introduce the structural interface algorithm for exact probabilistic inference in dynamic Bayesian networks. It unifies state-of-the-art techniques for inference in static and dynamic networks, by combining principles of knowledge compilation with the interface algorithm. The resulting algorithm not only exploits the repeated structure in the network, but also the local structure, including ...