The goal of this research is to stretch the computational limits of Bayesian inference with focus on embedded, real-time problems. Bayesian probability, as both a theoretical framework and as a representational and computational method, is having a profound impact on artificial intelligence research and practice. However, limitations is available computational methods still hamper widespread application, especially in real- time and embedded systems. This research builds from a core focus on search-based approximation methods, using real-time decision evaluation as its experimental tool, and extends to scaling issues including compilation (including reinforcement-learning) and control of communication in distributed systems. Results of this research will include improved methods for real-time monitoring and assessment for industrial, medical and consumer applications.
