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BBNBig Blue Nation (University of Kentucky Wildcats)
BBNBible Broadcasting Network (Christian radio network)
BBNBolt, Beranek, & Newman
BBNBig Bang Nucleosynthesis (cosmology)
BBNBye Bye Now
BBNBroadband Network
BBNBusiness Brokers Network
BBNBusiness Building Network
BBNBayesian Belief Network
BBNBackbone Network
BBNBarbarian (Dungeons and Dragons class)
BBNBaltic Business News
BBNBruin Broadcast Network (Catholic High School)
BBNBig Brother News
BBNBroadband Noise
BBNBuilding Better Neighborhoods (various locations)
BBNBayanihan Blog Network (Philippines)
BBNBellingham Business News (Bellingham, WA)
BBNBureau Burundais de Normalisation et Contrôle de la Qualité (French: Burundi Bureau of Standards and Quality Control; Burundi)
BBNBiuro Bezpieczenstwa Narodowego (Poland)
BBNBase-Band Node
BBNBulk Billing Number
BBNBurly Bear Network (TV)
BBNBeauBlancNoir (French: Beautiful White Black; sports club)
BBNBrass Band Normandie (France)
BBNBrethren Business Network (Organization of Church of the Brethren / Christian values in the workplace)
BBNBrèves Basse-Normandie (French: Short Lower Normandy)
BBNBlanc Beur Noir (French charitable organization)
References in periodicals archive ?
Greiner, "Learning bayesian belief network classifiers: Algorithms and system," in Advances in Artificial Intelligence.
NPS/NAVAIR Experimental UAS IFC Challenge - Phase II - Bayesian Belief Network Model Prototype, System Engineering Capstone Report, Naval Postgraduate School, Monterey, CA, USA, September 2014
In our research, Bayesian belief networks are developed with the help of a heuristic algorithm using the Bayesian function of network structure to distribution matching as a scoring function, named K2 (Jensen, 2001).
Bayesian Belief Networks, Bayesian Networks (BN) for short, are effective and practical representations of knowledge for reasoning under uncertainty.
Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction.
Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement, For.
Approximating probabilistic inference in bayesian belief networks is NP-hard.
IFR have used Bayesian Belief Networks to model hazards ranging from those associated with spore-forming bacteria to those attributed to natural toxicants.
Bayesian networks are also called as Bayesian Belief Networks (BBN), Belief Nets, Causal Probabilistic Nets (CPN) (Charniak, 1991).
Approximately probabilistic reasoning in Bayesian belief networks is NP-hard.
It will do so through a novel mixture of stakeholder participation in the construction of Bayesian Belief Networks, the collection of large scale socio-ecological data sets along gradients of land-use change, and their quantitative and qualitative analysis, leading to the creation of future scenarios of the impacts of land-use change.
Specific subjects examined include mining software repositories for traceability links, a hybrid program model for object-oriented reverse engineering, and using Bayesian belief networks to predict change propagation in software systems.
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