SCIENCE@home

Stratified sampling and bootstrapping for approximate Bayesian computation

Umberto Picchini,R. Everitt

Published 2019 in arXiv: Computation

ABSTRACT

Approximate Bayesian computation (ABC) is computationally intensive for complex model simulators. To exploit expensive simulations, data-resampling via bootstrapping was used with success in Everitt (2017) to obtain many artificial datasets at little cost and construct a synthetic likelihood. When using the same approach within ABC to produce a pseudo-marginal ABC-MCMC algorithm, the posterior variance is inflated, thus producing biased posterior inference. Here we use stratified Monte Carlo to considerably reduce the bias induced by data resampling. We also show that it is possible to obtain reliable inference using a larger than usual ABC threshold, by employing stratified Monte Carlo. Finally, we show that with stratified sampling we obtain a less variable ABC likelihood. We consider simulation studies for static (Gaussian, g-and-k distribution, Ising model) and dynamic models (Lotka-Volterra). For the Lotka-Volterra case study, we compare our results against a standard pseudo-Marginal ABC and find that our approach is four times more efficient and, given limited computational budget, it explores the posterior surface more thoroughly. A comparison against state-of-art sequential Monte Carlo ABC is also reported.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    arXiv: Computation

  • Publication date

    2019-05-20

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1905.07976
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-55 of 55 references · Page 1 of 1

CITED BY