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

Pedro Shiguihara, Alneu de Andrade Lopes, David Mauricio

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalIEEE Access
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
CCBY

Keywords

  • Bayes methods
  • Dynamic Bayesian Networks
  • dynamic probabilistic graphical models
  • Hidden Markov models
  • Licenses
  • literature review
  • Markov processes
  • Probabilistic logic
  • Probability distribution
  • systematic literature review
  • Systematics

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