A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction (2020)


Vihola, M., & Franks, J. (2020). On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika, 107(2), 381-395. https://doi.org/10.1093/biomet/asz078


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Vihola, Matti; Franks, Jordan

Lehti tai sarja: Biometrika

ISSN: 0006-3444

eISSN: 1464-3510

Julkaisuvuosi: 2020

Volyymi: 107

Lehden numero: 2

Artikkelin sivunumerot: 381-395

Kustantaja: Oxford University Press

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1093/biomet/asz078

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/69182

Julkaisu on rinnakkaistallennettu: https://arxiv.org/abs/1902.00412


Tiivistelmä

Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-processing the output, leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators and propose an adaptive approximate Bayesian computation Markov chain Monte Carlo algorithm, which finds a balanced tolerance level automatically based on acceptance rate optimization. Our experiments show that post-processing-based estimators can perform better than direct Markov chain Monte Carlo targeting a fine tolerance, that our confidence intervals are reliable, and that our adaptive algorithm leads to reliable inference with little user specification.


YSO-asiasanat: algoritmit; bayesilainen menetelmä; Markovin ketjut; Monte Carlo -menetelmät

Vapaat asiasanat: adaptive algorithm; approximate Bayesian computation; confidence interval; importance sampling;
Markov chain Monte Carlo; tolerance choice.


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2


Viimeisin päivitys 2021-09-08 klo 11:12