A Heuristic Independent Particle Approximation to Determinantal Point Processes

A determinantal point process is a stochastic that commonly used to capture negative correlations. It has become increasingly popular in machine learning recent years. Sampling however remains computationally intensive task. This note introduces heuristic independent particle approximation processes. The based on the physical intuition of fermions and implemented using standard numerical linear algebra routines. from this can be performed at negligible cost. Numerical results are provided demonstrate performance proposed algorithm.