Papers

Simulation-based inference via telescoping ratio estimation for trawl processes

D Leonte, R Huser, AED Veraart - arXiv preprint arXiv:2510.04042, 2025 - arxiv.org
Statistics paper stat.ML Suggest

… Simulation-based inference (SBI) offers a promising way forward, but existing methods typically require large training datasets or complex architectures and frequently …

Link to paper

BibTeX

@article{2510.04042v2,
Author        = {Dan Leonte and Raphaël Huser and Almut E. D. Veraart},
Title         = {Simulation-based inference via telescoping ratio estimation for trawl
  processes},
Eprint        = {2510.04042v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {The growing availability of large and complex datasets has increased interest
in temporal stochastic processes that can capture stylized facts such as
marginal skewness, non-Gaussian tails, long memory, and even non-Markovian
dynamics. While such models are often easy to simulate from, parameter
estimation remains challenging. Simulation-based inference (SBI) offers a
promising way forward, but existing methods typically require large training
datasets or complex architectures and frequently yield confidence (credible)
regions that fail to attain their nominal values, raising doubts on the
reliability of estimates for the very features that motivate the use of these
models. To address these challenges, we propose a fast and accurate,
sample-efficient SBI framework for amortized posterior inference applicable to
intractable stochastic processes. The proposed approach relies on two main
steps: first, we learn the posterior density by decomposing it sequentially
across parameter dimensions. Then, we use Chebyshev polynomial approximations
to efficiently generate independent posterior samples, enabling accurate
inference even when Markov chain Monte Carlo methods mix poorly. We further
develop novel diagnostic tools for SBI in this context, as well as post-hoc
calibration techniques; the latter not only lead to performance improvements of
the learned inferential tool, but also to the ability to reuse it directly with
new time series of varying lengths, thus amortizing the training cost. We
demonstrate the method's effectiveness on trawl processes, a class of flexible
infinitely divisible models that generalize univariate Gaussian processes,
applied to energy demand data.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.04042v2},
File          = {2510.04042v2.pdf}
}

Share