Non-Parametric Bayesian Belief Nets versus Vines
Anca Maria Hanea · Dependence Modeling · 2010
AbstractThis chapter reviews aspects of non-parametric Bayesian belief nets (NPBBN). The theory behind NPBBNs is closely related to that of regular vines and it has benefited from developments in the latter. It also offers an alternative to undirected graphical models in general, and to regular vines in particular. The differences and similarities in modeling using directed versus undirected graphs are discussed in this chapter from the perspective of NPBBNs and vines. Until recently, Bayesian belief nets (BBNs) were either discrete or discrete-normal. Despite their popularity, both suffer from severe limitations. Discrete BBNs are limited by size and complexity, discrete-normal BBNs are limited by the assumption of joint normality. NPBBNs were introduced to overcome these limitations. Algorithms for specifying, sampling and analyzing high-dimensional distributions using NPBBNs have been developed and successfully applied in decision support systems.