Additive Bayesian network models are equivalent to
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. A
comprehensive set of documented case studies, numerical
accuracy/quality assurance exercises, and additional
documentation are available from the abn website.

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.

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.