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 with iid random effects). 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.