BibTeX

@article{2411.02728v2,
Author        = {Manuel Gloeckler and Shoji Toyota and Kenji Fukumizu and Jakob H. Macke},
Title         = {Compositional simulation-based inference for time series},
Eprint        = {2411.02728v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Amortized simulation-based inference (SBI) methods train neural networks on
simulated data to perform Bayesian inference. While this strategy avoids the
need for tractable likelihoods, it often requires a large number of simulations
and has been challenging to scale to time series data. Scientific simulators
frequently emulate real-world dynamics through thousands of single-state
transitions over time. We propose an SBI approach that can exploit such
Markovian simulators by locally identifying parameters consistent with
individual state transitions. We then compose these local results to obtain a
posterior over parameters that align with the entire time series observation.
We focus on applying this approach to neural posterior score estimation but
also show how it can be applied, e.g., to neural likelihood (ratio) estimation.
We demonstrate that our approach is more simulation-efficient than directly
estimating the global posterior on several synthetic benchmark tasks and
simulators used in ecology and epidemiology. Finally, we validate scalability
and simulation efficiency of our approach by applying it to a high-dimensional
Kolmogorov flow simulator with around one million data dimensions.},
Year          = {2024},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2411.02728v2},
File          = {2411.02728v2.pdf}
}