Abstract
Current structure learning practices in Bayesian networks have been developed to learn the structure between observable variables and learning latent parameters independently. One exception establishes a variant of EM for learning the structure of Bayesian networks in the presence of incomplete data. However, no method has demonstrated learning the influence structure between latent variables that describe (or are learned from) a number of observations. We present a method that learns a set of na¨ıve Bayes models (NBMs) independently given a partitioned set of observations, and then attempts to track the high-level influence structure between every NBM. The latent parameters of each model are then relearned to fine-tune the influence distribution between models for density estimation of new observations. Experimental results are provided which demonstrate the effectiveness of our non-parametric method. Applications of this method include knowledge discovery and density estimation in situations where we do not fully observe characteristics of the environment.
Benjamin Rosman
Lab Director
I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.