Factored Sampling For Efficient Tracking of Large Hybrid Systems

Abstract

This work presents a new approach to monitoring large dynamic systems. The approach is based on factored particles, which adapts particle filtering by factoring the system into weakly interacting subsystems and maintaining particles over the factors, thus allowing much larger systems to be tracked. Our approach, hybrid factored sampling, works with systems that involve both discrete and continuous variables, including systems where discrete variables depend on continuous parents. The framework lends itself to asynchronous inference—each factor can be reasoned about independently, and the factors joined only when there exists sufficient correlation between them. This allows us to reason about each factor at its appropriate time granularity. In addition, hybrid factored sampling exploits the factorization to provide tractable look-ahead prediction, allowing sampling from the posterior probability given new observations, and considerably improving performance. Empirical results show that hybrid factored sampling is an efficient and versatile method for inference in large hybrid systems.