We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does ...

In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the structural and probabilistic properties of the tiles so that the distribution of the resulting tiled network resembles the realworld distribution of the original tiles. By generating networks of various sizes one can study...

In this thesis, we developed a Bayesian approach to estimate the detailed composition of an unknown feedstock in a chemical plant by combining information from a few bulk measurements of the feedstock in the plant along with some detailed composition information of a similar feedstock that was measured in a laboratory. The complexity of the Bayesian model combined with the simplex-type constrai...

This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch­ and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL) principle, for a given (consistent) variable ordering. The algorithm exhaustively searches through all network structures and guarantees to find the network with the best MDL score. Prelimi­ nary e...

Refinement of Bayesian network structure is a topic that becomes more and more relevant. Some work have been done there, but one problem has not been considered yet – what to do when new data has less or more attributes than the existing model. In both cases data contains important knowledge and every effort must be made in order to extract it. A merging algorithm capable to deal with situation...

Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects variables a probabilistic way. "Deleting, reversing, moving, and inserting" is an approach finding best answer proposition problem algorithm. In Enhanced Surface Water Searching Technique, most hunting for water done by elephants during dry seasons. Pigeon Optimization...