Optimal Configuration for Identification of Parameters for Chloride Ingress Models Using Bayesian Networks

doi 10.5150/jngcgc.2014.077
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Editions Paralia

Related search

A Bayesian Network Framework for Statistical Characterisation of Model Parameters From Accelerated Tests: Application to Chloride Ingress Into Concrete

Structure and Infrastructure Engineering
BuildingGeotechnical EngineeringRiskMechanical EngineeringCivilOcean EngineeringEngineering GeologyConstructionSafetyStructural EngineeringReliabilityQuality
2017English

Identification of Wiener Models Using Optimal Local Linear Models

Simulation Modelling Practice and Theory
HardwareModelingSoftwareArchitectureSimulation
2008English

Sensitivity Analysis of Chloride Ingress Models: Case of Concretes Immersed in Seawater

Construction and Building Materials
BuildingCivilConstructionStructural EngineeringMaterials Science
2017English

Acquisition of Causal Models for Local Distributions in Bayesian Networks

IEEE Transactions on Cybernetics
ControlSystems EngineeringInformation SystemsHuman-Computer InteractionElectronic EngineeringComputer Science ApplicationsElectricalSoftware
2014English

Influence of Priors in Bayesian Estimation of Genetic Parameters for Multivariate Threshold Models Using Gibbs Sampling

Genetics, Selection, Evolution
EvolutionEcologyGeneticsSystematicsAnimal ScienceMedicineBehaviorZoology
2007English

Learning Locally Minimax Optimal Bayesian Networks

International Journal of Approximate Reasoning
Applied MathematicsTheoretical Computer ScienceArtificial IntelligenceSoftware
2010English

Bayesian Hierarchical Models for Protein Networks in Single-Cell Mass Cytometry

Cancer Informatics
Cancer ResearchOncology
2014English

Using Bayesian Networks for Selecting Classifiers in GP Ensembles

Information Sciences
ControlSystems EngineeringManagementTheoretical Computer ScienceComputer Science ApplicationsInformation SystemsArtificial IntelligenceSoftware
2014English

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India)
Electronic EngineeringElectrical
2017English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy