期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 28, 期 1, 页码 205-219出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2018.1497511
关键词
Adaptive importance sampling; Approximate Bayesian computation; Likelihood-free inference; Quasi-Monte Carlo; Randomized quasi-Monte Carlo
资金
- GENES doctoral scholarship
- French National Research Agency (ANR) as part of the Investissements d'Avenir program [ANR-11-LABEX-0047]
ABC (approximate Bayesian computation) is a general approach for dealing with models with an intractable likelihood. In this work, we derive ABC algorithms based on QMC (quasi-Monte Carlo) sequences. We show that the resulting ABC estimates have a lower variance than their Monte Carlo counter-parts. We also develop QMC variants of sequential ABC algorithms, which progressively adapt the proposal distribution and the acceptance threshold. We illustrate our QMC approach through several examples taken from the ABC literature.
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