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Benchmarking dynamic Bayesian network structure learning algorithms
2013
2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)
Dynamic Bayesian Networks (DBNs) are probabilistic graphical models dedicated to modeling multivariate time series. Two-time slice BNs (2-TBNs) are the most current type of these models. Static BN structure learning is a well-studied domain. Many approaches have been proposed and the quality of these algorithms has been studied over a range of different standard networks and methods of evaluation. To the best of our knowledge, all studies about DBN structure learning use their own benchmarks
doi:10.1109/icmsao.2013.6552549
fatcat:6xlz2xhh4ndpjbnoyndbwnc5xi