Data Modelling with Additive Bayesian Networks
Description
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.