This thesis introduces Higher-Order Bayesian networks (HOBNs), a probabilistic graphical model framework for inference and learning over structured data. HOBNs extend the expressive power of standard Bayesian networks with random variables ranging over domains of certain families of higher-order terms. The formalism allows the expression of conditional independence assumptions on the domain, which are exploited in order to give an efficient method of defining probability distributions over the higher-order types. Methods for probabilistic inference and model construction from data observations are discussed, and experimental results on real-world domains are presented.
|Translated title of the contribution||A Probabilistic Graphical Model Framework for Higher-Order Term-Based Representations|
|Publication status||Published - 2006|