Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Airport terminal-approach safety and capacity analysis using an agent-based model
WSC '04 Proceedings of the 36th conference on Winter simulation
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
A multivariate discretization method for learning Bayesian networks from mixed data
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A proposed validation framework for expert elicited Bayesian Networks
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
Quantifying Success Factors for IT Projects: An Expert-Based Bayesian Model
Information Systems Management
| Hi-index | 12.05 |
Validation is an important issue in the development and application of Bayesian Belief Network (BBN) models, especially when the outcome of the model cannot be directly observed. Despite this, few frameworks for validating BBNs have been proposed and fewer have been applied to substantive real-world problems. In this paper we adopt the approach by Pitchforth and Mengersen (2013), which includes nine validation tests that each focus on the structure, discretisation, parameterisation and behaviour of the BBNs included in the case study. We describe the process and result of implementing a validation framework on a model of a real airport terminal system with particular reference to its effectiveness in producing a valid model that can be used and understood by operational decision makers. In applying the proposed validation framework we demonstrate the overall validity of the Inbound Passenger Facilitation Model as well as the effectiveness of the validity framework itself.