Estimating dynamic models by matching random features
Abstract
Scientists increasingly express their ideas as dynamic models of complex processes. It is often much easier to simulate these models than to calculate the probability of their generating a particular outcome, making likelihood-based estimation infeasible. Existing likelihood-free approaches rely either on manually chosen summary statistics or on representations learned by neural networks. The former is error-prone and laborious, while the latter is computationally intensive, leaving many scientists in a difficult position. We show that, for a large class of dynamic models, parameters can be estimated by matching a small number of random features of the observed and simulated data. In particular, we show that models with a p-dimensional parameter can be identified from just 2p+1 generic random Fourier features. We introduce two estimators for stationary and nonstationary processes, respectively, and we establish their consistency under mild regularity conditions. More broadly, our results serve as the foundation for a new class of methods for simulation-based inference.
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BibTeX
@misc{wiecksosa2026estimating,
title = {{Estimating dynamic models by matching random features}},
author = {Michael Wieck-Sosa and Cosma Rohilla Shalizi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.21916},
eprint = {2607.21916},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.21916},
}
RIS
TY - UNPB
TI - Estimating dynamic models by matching random features
AU - Michael Wieck-Sosa
AU - Cosma Rohilla Shalizi
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2607.21916
UR - https://arxiv.org/abs/2607.21916
AB - Scientists increasingly express their ideas as dynamic models of complex processes. It is often much easier to simulate these models than to calculate the probability of their generating a particular outcome, making likelihood-based estimation infeasible. Existing likelihood-free approaches rely either on manually chosen summary statistics or on representations learned by neural networks. The former is error-prone and laborious, while the latter is computationally intensive, leaving many scientists in a difficult position. We show that, for a large class of dynamic models, parameters can be estimated by matching a small number of random features of the observed and simulated data. In particular, we show that models with a p-dimensional parameter can be identified from just 2p+1 generic random Fourier features. We introduce two estimators for stationary and nonstationary processes, respectively, and we establish their consistency under mild regularity conditions. More broadly, our results serve as the foundation for a new class of methods for simulation-based inference.
ER -