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Intensity-Free Learning of Temporal Point Processes
[article]
2020
arXiv
pre-print
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design
arXiv:1909.12127v2
fatcat:6fgz2rfrc5fabd3sszdyub6fiq