Data Modelling with Additive Bayesian Networks
Description
Additive Bayesian network models are equivalent to
Bayesian multivariate regression using graphical modelling.
This library provides routines to help determine optimal
Bayesian network models for a given data set, where these
models are used to identify statistical dependencies in messy,
complex data. The additive formulation of these models is
equivalent to multivariate generalised linear modelling
(including mixed models). The usual term to describe this model
selection process is structure discovery. The core
functionality is concerned with model selection - determining
the most robust empirical model of data from interdependent
variables. Laplace approximations are used to estimate goodness
of fit metrics and model parameters, and wrappers are also
included to the INLA library. A comprehensive set of documented
case studies, numerical accuracy/quality assurance exercises,
and additional documentation are available from the abn
website.