Predictive modeling of everyday behavior from large-scale data

— Learning and inference from Bayesian networks based on actual services —

Abstract

Daily life behavior modeling is discussed. This modeling framework consists of statistical learning, probabilistic reasoning, user modeling, and large-scale data collecting technologies. Bayesian networks can represent causality relationship as graphical structures. Such models should include situations and contexts of daily life behavior through real services. In order to collect large-scale data connected with them, we have to provide real services supported by many users. This concept is named "Research as a service" and discussed in this paper.