BibTeX

@article{2404.13557v1,
Author        = {Xiaoyu Wang and Ryan P. Kelly and David J. Warne and Christopher Drovandi},
Title         = {Preconditioned Neural Posterior Estimation for Likelihood-free Inference},
Eprint        = {2404.13557v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation based inference (SBI) methods enable the estimation of posterior
distributions when the likelihood function is intractable, but where model
simulation is feasible. Popular neural approaches to SBI are the neural
posterior estimator (NPE) and its sequential version (SNPE). These methods can
outperform statistical SBI approaches such as approximate Bayesian computation
(ABC), particularly for relatively small numbers of model simulations. However,
we show in this paper that the NPE methods are not guaranteed to be highly
accurate, even on problems with low dimension. In such settings the posterior
cannot be accurately trained over the prior predictive space, and even the
sequential extension remains sub-optimal. To overcome this, we propose
preconditioned NPE (PNPE) and its sequential version (PSNPE), which uses a
short run of ABC to effectively eliminate regions of parameter space that
produce large discrepancy between simulations and data and allow the posterior
emulator to be more accurately trained. We present comprehensive empirical
evidence that this melding of neural and statistical SBI methods improves
performance over a range of examples, including a motivating example involving
a complex agent-based model applied to real tumour growth data.},
Year          = {2024},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2404.13557v1},
File          = {2404.13557v1.pdf}
}