Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 28, Issue 1, Pages 205-219Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2018.1497511
Keywords
Adaptive importance sampling; Approximate Bayesian computation; Likelihood-free inference; Quasi-Monte Carlo; Randomized quasi-Monte Carlo
Categories
Funding
- GENES doctoral scholarship
- French National Research Agency (ANR) as part of the Investissements d'Avenir program [ANR-11-LABEX-0047]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available