Abstract In this paper, a distribution network topology identification algorithm based on mutual information Bayesian is proposed. algorithm, the model mainly used to fit nonlinear relationship among various variables such as photovoltaic, load, measured voltage, and topology. Then, effectively divide measurement continuous stage intervals of variables, thus improving analysis accuracy by netwo...

A dynamic Bayesian network data fusion algorithm for estimating leaf area index using time-series data from in situ measurement to remote sensing observations Yonghua Qu a b , Yuzhen Zhang a b & Jindi Wang a b a State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100875...

Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal influences of the first order, i.e., influences between neighboring time steps. This choice is a convenient approximation influenced by the existence of efficient algorithms for first order models and limitations of available tools. We focus on the question whether constructing higher time-order models is worth the effort ...

In order to solve the problem of modeling and inference for a vast complicated system, a new concept of Hierarchy DDBN was proposed here through the layer analysis method, and worked out the inference algorithm of the Hierarchy DDBN based on the strict probability theory. In order to test the validity of the inference algorithm, a series of simulation were conducted. The result showed that the ...

In Bayesian network structure learning for incomplete data set, a common problem is too many attributes causing low efficiency and high computation complexity. In this paper, an algorithm of attribute reduction based on rough set is introduced. The algorithm can effectively reduce the dimension of attributes and quickly determine the network structure using mutual information for Bayesian netwo...

The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate...

This paper describes a novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data. A Bayesian network is a graphical knowledge representation tool. However, learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, whi...