Belief Propagation and Beyond for Particle Tracking

Understand

We describe a novel approach to statistical learning from particles tracked while moving in a random environment.

• The problem consists in inferring properties of the environment from recorded snapshots.
• We consider here the case of a fluid seeded with identical passive particles that diffuse and are advected by a flow.
• Our approach rests on efficient algorithms to estimate the weighted number of possible matchings among particles in two consecutive snapshots, the partition function of the underlying graphical model.