An Incremental Probability Model for Dynamic Systems
Wolf Kohn, Philip Charles Placek, Zelda B. Zabinsky, Jonathan Cross · IEEE Transactions on Systems Man and Cybernetics Systems · 2018
In this paper, we present an incremental probability model for dynamic systems. This model combines historical data and new, real-time sequential data as it becomes available. Traditionally, models and algorithms assume the data follows a Gaussian distribution or other specified form. Instead, we propagate the transition probabilities directly which allows us to build the probability distribution from data. This provides a more realistic algorithm for probabilistic forecasting. To address large scale problems, our method is made computationally efficient by using an incremental model to construct probabilities relative to the nominal (or mean) of the state which is allowed to change over time. A mean-field approach further reduces computation while preserving statistical dependencies.