A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Solves inconsistent estimation for Neyman-Scott problems.
A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.
Develops variational inference for Neyman-Scott processes for faster sampling.
Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.
Strict Minimum Message Length (SMML) is an information-theoretic statistical inference method widely cited (but only with informal arguments) as providing estimations that are consistent for general estimation problems. It is, however, almost invariably intractable to compute, for which reason only approximations of it…
Study sharp convergence rates of empirical UOT for spatio-temporal point processes.
Modeling rainfall with a flexible Hawkes process.