Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stratified sampling and bootstrapping for approximate Bayesian
  computation   Approximate Bayesian computation (ABC) is computationally intensive for
complex model simulators. To exploit expensive simulations, data-resampling via
bootstrapping can be employed to obtain many artificial datasets at little
cost. However, when using this approach within ABC, the posterior variance is
inflated, thus resulting in biased posterior inference. Here we use stratified
Monte Carlo to considerably reduce the bias induced by data resampling. We also
show empirically that it is possible to obtain reliable inference using a
larger than usual ABC threshold. Finally, we show that with stratified Monte
Carlo we obtain a less variable ABC likelihood. Ultimately we show how our
approach improves the computational efficiency of the ABC samplers. We
construct several ABC samplers employing our methodology, such as rejection and
importance ABC samplers, and ABC-MCMC samplers. We consider simulation studies
for static (Gaussian, g-and-k distribution, Ising model, astronomical model)
and dynamic models (Lotka-Volterra). We compare against state-of-art sequential
Monte Carlo ABC samplers, synthetic likelihoods, and likelihood-free Bayesian
optimization. For a computationally expensive Lotka-Volterra case study, we
found that our strategy leads to a more than 10-fold computational saving,
compared to a sampler that does not use our novel approach.

1
The categories of this paper are: cs
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th