Bayesian network

Controls of variability in berm and dune storm erosion

The erosion impact of large coastal storm events typically occurs across broad (100s of km) sections of coastline and may include significant variability both alongshore and vertically between the berm and dunes. Identifying controls of variability …

A variable selection package driving Netica with Python

Bayesian Networks (BNs) are useful methods of probabilistically modelling environmental systems. BN performance is sensitive to the number of variables included in the model framework. The selection of the optimum set of variables to include in a BN …

A comparison of methods for discretizing continuous variables in Bayesian Networks

Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and …

Bayesian Networks in coastal engineering: Distinguishing descriptive and predictive applications

Bayesian networks (BNs) are increasingly being used to model complex coastal processes due to their ability to integrate non-linear systems, their transparent probabilistic framework, and low computational cost. A BN may be suited to descriptive or …

Predicting storm erosion on sandy coastlines using a Bayesian network

Bayesian Networks (BNs) are increasingly being used to model coastal processes. BNs are probabilistic graphical models that are able to represent complex physical systems with the benefits of very low computational cost, intrinsic handling of …