Gaussian Lifted Marginal Filtering

Abstract

Recently, Lifted Marginal Filtering [5] has been proposed, an approach for efficient probabilistic inference in systems with multiple, (inter-)acting agents and objects (entities). The algorithm achieves its efficiency by performing inference jointly over groups of similar entities (i.e. their properties follow the same distribution). In this paper, we explore the case where there are no entities that are directly suitable for grouping. We propose to use methods from Gaussian mixture fitting and merging to identify entity groups and keep the number of groups constant over time. Empirical results suggest that decrease in prediction accuracy is small, while the algorithm runtime decreases significantly.