Results 51 to 60 of about 39,041 (304)
Dynamic Bayesian Networks for Audio-Visual Speech Recognition
EURASIP Journal on Advances in Signal Processing, 2002 The use of visual features in audio-visual speech recognition (AVSR) is justified by both the speech generation mechanism, which is essentially bimodal in audio and visual representation, and by the need for features that are invariant to acoustic noise
Liang Luhong +4 more
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Learning dynamic Bayesian networks [PDF]
, 1998 Bayesian networks are a concise graphical formalism for describing probabilistic models. We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian networks. In many of the interesting models, beyond the simple linear dynamical system or hidden Markov model, the calculations required for inference are intractable.
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DYNAMIC BAYESIAN NETWORKS IN SYSTEM RELIABILITY ANALYSIS [PDF]
IFAC Proceedings Volumes, 2006 Today industrial systems are characterized by a set of dependencies among the components and the environment of the system. To address these difficulties, this paper presents a method for modelling and analyzing the reliability of a complex system based on Dynamic Bayesian Networks (DBN). This method allows to take into account the influence of time or
Ben Salem, Abdeljabbar +2 more
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Prognostic Modelling with Dynamic Bayesian Networks [PDF]
, 2009 In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not ...
McNaught, Ken R., Zagorecki, A.
core
FEBS Open Bio, EarlyView.Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
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dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R
Journal of Statistical SoftwareDynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation.
David Quesada +2 more
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Transmembrane topology and signal peptide prediction using dynamic bayesian networks. [PDF]
PLoS Computational Biology, 2008 Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction.
Sheila M Reynolds +4 more
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Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference.
Yang Xiang, Hanwen Zheng
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Infinite Dynamic Bayesian Networks. [PDF]
, 2011 United States. Air Force Office of Scientific Research (AFOSR FA9550-07-1-0075)
Doshi-Velez, Finale P. +3 more
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The Computational Power of Dynamic Bayesian Networks [PDF]
CoRR, 2016 This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-complete computation.
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