Results 221 to 230 of about 42,084 (260)
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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.
Virtanen, Kai, Poropudas, Jirka
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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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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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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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Causal Discovery of Dynamic Bayesian Networks
2012While a great variety of algorithms have been developed and applied to learning static Bayesian networks, the learning of dynamic networks has been relatively neglected. The causal discovery program CaMML has been enhanced with a highly flexible set of methods for taking advantage of prior expert knowledge in the learning process.
Cora Beatriz Pérez-Ariza +4 more
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2010
Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled.
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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled.
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Intrinsic Learning of Dynamic Bayesian Networks
2014Programs that learn Bayesian networks normally learn directed acyclic graphs (DAGs) of arbitrary structure, including those with repeating structures, such as dynamic Bayesian networks (DBNs). Perhaps for that reason there is relatively little literature on learning DBNs specifically and more focusing on applying general learners to the task.
Alex Black +2 more
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Dynamic Bayesian Networks for Language Modeling
2006Although n-gram models are still the de facto standard in language modeling for speech recognition, it has been shown that more sophisticated models achieve better accuracy by taking additional information, such as syntactic rules, semantic relations or domain knowledge into account Unfortunately, most of the effort in developing such models goes into ...
Pascal Wiggers, Léon J. M. Rothkrantz
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