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How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions. [PDF]
Bull Math BiolRabiei K +3 more
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Bayesian compression for dynamically expandable networks
Pattern Recognition, 2022Abstract This paper develops Bayesian Compression for Dynamically Expandable Network (BCDEN), which can learn a compact model structure with preserving the accuracy in a continual learning scenarios. Dynamically Expandable Network (DEN) is efficiently trained by performing selective retraining, dynamically expands network capacity with only the ...
Yang Yang 0072 +2 more
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Simulation metamodeling with dynamic Bayesian networks
European Journal of Operational Research, 2011This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions.
Kai Virtanen, Jirka Poropudas
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2021
Underground transportation systems are in great demand in many large cities all over the world. Tunnel construction has presented a powerful momentum for rapid economic development worldwide. However, owing to various risk factors in complex project environments, safety violations occur frequently in tunnel construction, leading to large problems on ...
Limao Zhang +3 more
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Underground transportation systems are in great demand in many large cities all over the world. Tunnel construction has presented a powerful momentum for rapid economic development worldwide. However, owing to various risk factors in complex project environments, safety violations occur frequently in tunnel construction, leading to large problems on ...
Limao Zhang +3 more
openaire +1 more source
Topological Dynamic Bayesian Networks
2010 20th International Conference on Pattern Recognition, 2010The objective of this research is to embed topology within the dynamic Bayesian network (DBN) formalism. This extension of a DBN (that encodes statistical or causal relationships) to a topological DBN (TDBN) allows continuous mappings (e.g., topological homeomorphisms), topological relations (e.g., homotopy equivalences) and invariance properties (e.g.,
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International Journal of Intelligent Systems, 2004
Summary: We extend Darwiche's differential approach to inference in Bayesian Networks (BNs) to handle specific problems that arise in the context of Dynamic Bayesian Networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial.
Boris Brandherm, Anthony Jameson
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Summary: We extend Darwiche's differential approach to inference in Bayesian Networks (BNs) to handle specific problems that arise in the context of Dynamic Bayesian Networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial.
Boris Brandherm, Anthony Jameson
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Dynamic Bayesian Networks for Student Modeling
IEEE Transactions on Learning Technologies, 2017Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling.
Tanja Käser +3 more
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Research on modeling with dynamic Bayesian networks
Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003), 2004For simplicity of calculation, dynamic Bayesian networks (DBNs) make assumptions that their evolvement follows Markov process and the transition probabilities in the evolvement are time-invariant. While this is not the case in many real complex systems.
Fengzhan Tian, Hongwei Zhang, Yuchang Lu
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Dynamic Bayesian networks for visual recognition of dynamic gestures
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2002Summary: Dynamic Bayesian networks are a powerful representation to describe processes that vary over time inside a stochastic framework. This paper describes an online visual recognition system to recognize a set of five dynamic gestures executed with the user's right hand using dynamic Bayesian networks for recognition.
Héctor Hugo Avilés-Arriaga +1 more
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