Learning first-order probabilistic models with combining rules

Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relationships. To keep these models succinct, each such influence is considered independent of others, which is called the assumption of "independence of causal influences" (ICI). In this paper, we describe a language that consists of quantified conditional influence statements and captures most relational probabilistic models based on directed graphs. The influences due to different statements are combined using a set of combining rules such as Noisy-OR. We motivate and introduce multi-level combining rules, where the lower level rules combine the influences due to different ground instances of the same statement, and the upper level rules combine the influences due to different stateme...

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar
• Keywords: Conditional Influence Statements, ICML 2005, Lower Level Rules, Machine Learning, Multi-level Combining Rules