BibTeX

@article{2112.11971v1,
Author        = {Thomas P Prescott and David J Warne and Ruth E Baker},
Title         = {Efficient Multifidelity Likelihood-Free Bayesian Inference with Adaptive
  Computational Resource Allocation},
Eprint        = {2112.11971v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.CO},
Abstract      = {Likelihood-free Bayesian inference algorithms are popular methods for
calibrating the parameters of complex, stochastic models, required when the
likelihood of the observed data is intractable. These algorithms
characteristically rely heavily on repeated model simulations. However,
whenever the computational cost of simulation is even moderately expensive, the
significant burden incurred by likelihood-free algorithms leaves them unviable
in many practical applications. The multifidelity approach has been introduced
(originally in the context of approximate Bayesian computation) to reduce the
simulation burden of likelihood-free inference without loss of accuracy, by
using the information provided by simulating computationally cheap, approximate
models in place of the model of interest. The first contribution of this work
is to demonstrate that multifidelity techniques can be applied in the general
likelihood-free Bayesian inference setting. Analytical results on the optimal
allocation of computational resources to simulations at different levels of
fidelity are derived, and subsequently implemented practically. We provide an
adaptive multifidelity likelihood-free inference algorithm that learns the
relationships between models at different fidelities and adapts resource
allocation accordingly, and demonstrate that this algorithm produces posterior
estimates with near-optimal efficiency.},
Year          = {2021},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2112.11971v1},
File          = {2112.11971v1.pdf}
}