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