BibTeX

@article{2111.09220v1,
Author        = {Cosma Rohilla Shalizi},
Title         = {A Note on Simulation-Based Inference by Matching Random Features},
Eprint        = {2111.09220v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {We can, and should, do statistical inference on simulation models by
adjusting the parameters in the simulation so that the values of {\em randomly
chosen} functions of the simulation output match the values of those same
functions calculated on the data. Results from the "state-space reconstruction"
or "geometry from a time series'' literature in nonlinear dynamics indicate
that just $2d+1$ such functions will typically suffice to identify a model with
a $d$-dimensional parameter space. Results from the "random features"
literature in machine learning suggest that using random functions of the data
can be an efficient replacement for using optimal functions. In this
preliminary, proof-of-concept note, I sketch some of the key results, and
present numerical evidence about the new method's properties. A separate,
forthcoming manuscript will elaborate on theoretical and numerical details.},
Year          = {2021},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2111.09220v1},
File          = {2111.09220v1.pdf}
}