  Determinantal point processes (DPPs) are probabilistic models for repulsion.
When used to represent the occurrence of random subsets of a finite base set,
DPPs allow to model global negative associations in a mathematically elegant
and direct way. Discrete DPPs have become popular and computationally tractable
models for solving several machine learning tasks that require the selection of
diverse objects, and have been successfully applied in numerous real-life
problems. Despite their popularity, the statistical properties of such models
have not been adequately explored. In this note, we derive the Markov
properties of discrete DPPs and show how they can be expressed using graphical
models.
