Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance a...

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Veröffentlicht in:IEEE access Jg. 9; S. 117639 - 117648
Hauptverfasser: Shiguihara, Pedro, Lopes, Alneu De Andrade, Mauricio, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bayes methods
Bayesian analysis
Dynamic Bayesian networks
dynamic probabilistic graphical models
Hidden Markov models
Inference
literature review
Literature reviews
Machine learning
Markov processes
Modelling
Probabilistic logic
Probability distribution
systematic literature review
Systematics
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997-2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3105520