Results 41 to 50 of about 5,728,670 (281)
Independence Decomposition in Dynamic Bayesian Networks [PDF]
, 2007 Dynamic Bayesian networks are a special type of Bayesian network that explicitly incorporate the dimension of time. They can be distinguished into repetitive and non-repetitive networks. Repetitiveness implies that the set of random variables of the network and their independence relations are the same at each time step.
Flesch, I., Lucas, Peter
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Reliability based rehabilitation of water distribution networks by means of Bayesian networks
Journal of Water and Land Development, 2017 Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN). The main elements of water distribution network (
Lakehal Abdelaziz, Laouacheria Fares
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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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A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms
Discrete Dynamics in Nature and Society, 2022 In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based ...
Wei Zhou
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Gene networks inference using dynamic Bayesian networks [PDF]
Bioinformatics, 2003 Abstract This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed.
Bruno-Edouard Perrin +5 more
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Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition [PDF]
, 2011 This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated.
Alkhateeb, Jawad H. +3 more
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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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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
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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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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
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