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Determinantal Point Processes for Machine Learning
2012
Foundations and Trends® in Machine Learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms,
doi:10.1561/2200000044
fatcat:qqq2fvo6tfcnbd73ee2oa43num