Statistics and Computing
Statistics and Computing
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Statistics and Computing
Particle Algorithms for Optimization on Binary Spaces
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
On the choice of MCMC kernels for approximate Bayesian computation with SMC samplers
Proceedings of the Winter Simulation Conference
Optimal parallelization of a sequential approximate Bayesian computation algorithm
Proceedings of the Winter Simulation Conference
Likelihood-free parallel tempering
Statistics and Computing
Adaptive approximate Bayesian computation for complex models
Computational Statistics
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Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Most effective SMC algorithms that are currently available for ABC have a computational complexity that is quadratic in the number of Monte Carlo samples (Beaumont et al., Biometrika 86:983---990, 2009; Peters et al., Technical report, 2008; Toni et al., J. Roy. Soc. Interface 6:187---202, 2009) and require the careful choice of simulation parameters. In this article an adaptive SMC algorithm is proposed which admits a computational complexity that is linear in the number of samples and adaptively determines the simulation parameters. We demonstrate our algorithm on a toy example and on a birth-death-mutation model arising in epidemiology.