ABI estimates the distance between posterior distributions via conditional quantile regression and uses adaptive rejection sampling with generative proposals to produce likelihood-free posterior approximations that converge to the true posterior.
On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
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abstract
We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a combination of Gibbs sampling and simple Metropolis updates together with three novel "shift" and "scale" updates. We show that our novel updates improve the speed of sampling considerably, by factors of about 60 to about 180 on a variety of simulated data sets.
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Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching
ABI estimates the distance between posterior distributions via conditional quantile regression and uses adaptive rejection sampling with generative proposals to produce likelihood-free posterior approximations that converge to the true posterior.