Li Z, Li P, Krishnan A, Liu JD (2011) Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics 27 2686-2691. doi 10.1093/ bioinformatics/btr454... [Pg.550]

Kim S. Y., Imoto S. and Miyano S. (2003). Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics. 4, pp 228-235. [Pg.398]

Perrin B. E., Ralaivola L., Mazurie A., Bottani S., Mallet J. and dAlche-Buc F. (2003). Gene network inference using dynamic Bayesian networks. Bioinformatics. 19, pp 138-142. [Pg.399]

Zou M. and Conzen S. D. (2004). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 21, pp. 71-79. [Pg.400]

Dynamic Bayesian Networla A dynamic Bayesian network (DBN) is an extension of the standard Bayesian network to a model with cyclic causal relationships among temporal time-series biological variables. While classic Bayesian networks are based on static data, DBN can use time sequential data for constructing causal relationships. Since there are many feedback processes in biological systems, it is important to consider such feedback loops in DBN. The joint probabihty distribution of DBN can be represented as... [Pg.269]

BARNACLE 7 dihedral angles Dynamic Bayesian network Maximum likelihood estimation Monte Carlo [27]... [Pg.523]

Regulatory Interactions from Microarray Experiments with Dynamic Bayesian Networks. [Pg.409]

Neil M., Hager D, Andersen L. B. (2009) Modelling Risk in Financial Institutions using Hybrid Dynamic Bayesian Networks, 4(1) 1-31. [Pg.75]

System health management is based on several criteria and parameters but one of the most important is the reliability analysis of the system. (Ramirez et al. 2006) presents a generic method for reliability estimation through BNs, otherwise a specific Bayesian model has been proposed for the multistate system by (Zhou et al. 2006). (Weber et al. 2003) presents Dynamic Bayesian Networks (DBNs) for the reliability modeling as well as (Weber et al. 2005) proposes Dynamic Object Oriented Bayesian Networks (DOOBNs) for the representation of the complex system reliability. [Pg.224]

Montani, S., Portinale, L., Bobbio, A., Varesio, M., Codetta-Raiteri, D., 2006. A tool for automatically translating dynamic fault trees into dynamic bayesian networks. Reliability and Maintainability Symposium, 2006, RAMS 06 Annual, Pages 434-441. [Pg.228]

Weber, R, Jouffe, L., 2003. Reliability modelling with Dynamic Bayesian Networks. Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. [Pg.228]

Jinqiu Hu, Laibin Zhang, Lin Ma, Wei Liang., 2011. An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm Expert Systems with Applications, 1431-1446. [Pg.161]

Montani S., Portinale L., Bobbio A., Codetta-Raiteri D. (2008). RADYBAN a tool for reliability analysis of dynamic fault trees throughconversion into dynamic Bayesian networks. Reliability Engineering and System Safety, Vol. 93, 922-932. [Pg.557]

The surveyed DRM methods are Dynamic Event Trees Dynamic Flowgraph Methodology Discrete state-transition approaches (Markov chains, Petri Nets extensions) Dynamic Bayesian Networks Direct system simulation DRM for aircraft certification TOPAZ (Traffic Organization and Perturbation Analyzer) SoTeRia (Socio-Technical Risk Analysis) FRAM (Functional Resonance Analysis Method) STPA Hazard analysis (Systems-Theoretic Process Analysis) Collision Risk Modelling Encounter-based model methodology. For references and brief descriptions of all these methods, refer to (DRM D04 2012). [Pg.731]

Straub, D. 2009. Stochastic modeling of deterioration processes through dynamic Bayesian networks. Journal of Engineering Mechanics, 135, 1089-1099. [Pg.782]

Cai, B., Liu, Y., Zhang, Y, Fan, Q., Liu, Z. Tian, X. 2013. A dynamic Bayesian networks modelling of human factors on offshore blowouts. Journal of Loss Prevention in the Process Industries 639-649. [Pg.1079]

BN is a probabilistic approach that can be used for modeling behaviors of a system, based on observed stochastic events. A BN is a model representing the interactions among components in a system from a probabilistic perspective (Di Giorgio Liberati 2011). One of major disadvantages of this approach is its difficulty of taking time into account. Therefore, an improved approach. Dynamic Bayesian Network (DBN), has been developed. In order to analyze Cl interdependencies, the general BN and DBN represent system components as variables and interactions between variables as directed probabilistic links. BN or even DBN approach is not suitable to analyze dynamic, often non-linear, behaviors of infrastructure systems. [Pg.2062]

Di Giorgio A. Liberati F. 2011. Interdependency modeling and analysis of critical infrastructures based on Dynamic Bayesian Networks. I9th Mediterranean Conferenceon Control Automation (MED) 19 1. [Pg.2066]

© 2019 chempedia.info