Abstract
Generally, Bayesian networks are constructed either from the available information or starting from a naive Bayes. In the medical domain, some systems refine Bayesian networks manually created by domain experts. However, existing techniques verify the relation of a node with every other node in the network. In this work, we define a Refinement algorithm that verifies the relation of a node only with the set of its independent nodes using Markov assumption, instead of selecting two nodes randomly. Refinement algorithm considers minimal updates to the original network and shows that less number of comparisons is needed to find the best network structure.
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Bhimagavni, N.K., Kumar, P.V. (2019). Refinement of Manually Built Bayesian Networks Created by Domain Experts Based on Markov Assumption. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 32. Springer, Singapore. https://doi.org/10.1007/978-981-10-8201-6_19
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DOI: https://doi.org/10.1007/978-981-10-8201-6_19
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