BibTeX

@article{2311.09446v3,
Author        = {Joonha Park},
Title         = {Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator},
Eprint        = {2311.09446v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Models implicitly defined through a random simulator of a process have become widely used in scientific and industrial applications in recent years. However, simulation-based inference methods for such implicit models, like approximate Bayesian computation (ABC), often scale poorly as data size increases. We develop a scalable inference method for implicitly defined models using a metamodel for the Monte Carlo log-likelihood estimator derived from simulations. This metamodel characterizes both statistical and simulation-based randomness in the distribution of the log-likelihood estimator across different parameter values. Our metamodel-based method quantifies uncertainty in parameter estimation in a principled manner, leveraging the local asymptotic normality of the mean function of the log-likelihood estimator. We apply this method to construct accurate confidence intervals for parameters of partially observed Markov process models where the Monte Carlo log-likelihood estimator is obtained using the bootstrap particle filter. We numerically demonstrate that our method enables accurate and highly scalable parameter inference across several examples, including a mechanistic compartment model for infectious diseases.},
Year          = {2023},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2311.09446v3},
File          = {2311.09446v3.pdf}
}