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Particle gradient descent model for point process generation. (arXiv:2010.14928v3 [stat.ML] UPDATED)
cs.LG updates on arXiv.org arxiv.org
This paper presents a statistical model for stationary ergodic point
processes, estimated from a single realization observed in a square window.
With existing approaches in stochastic geometry, it is very difficult to model
processes with complex geometries formed by a large number of particles.
Inspired by recent works on gradient descent algorithms for sampling
maximum-entropy models, we describe a model that allows for fast sampling of
new configurations reproducing the statistics of the given observation.
Starting from an initial random …