Adaptively learning the auxiliary ellipse distribution in elliptical slice sampling gives a gradient-free sampler that is ergodic under stated assumptions and empirically competitive with HMC and adaptive random walks on challenging posteriors.
Air Markov Chain Monte Carlo
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We introduce a class of Adapted Increasingly Rarely Markov Chain Monte Carlo (AirMCMC) algorithms where the underlying Markov kernel is allowed to be changed based on the whole available chain output but only at specific time points separated by an increasing number of iterations. The main motivation is the ease of analysis of such algorithms. Under the assumption of either simultaneous or (weaker) local simultaneous geometric drift condition, or simultaneous polynomial drift we prove the $L_2-$convergence, Weak and Strong Laws of Large Numbers (WLLN, SLLN), Central Limit Theorem (CLT), and discuss how our approach extends the existing results. We argue that many of the known Adaptive MCMC algorithms may be transformed into the corresponding Air versions, and provide an empirical evidence that performance of the Air version stays virtually the same.
years
2026 3representative citing papers
Explicit MSE bounds derived for time-average estimators in adaptive increasingly rare MCMC under simultaneous Wasserstein contraction.
CBARA integrates response-adaptive and covariate-adaptive randomization via a new imbalance vector and pseudo-Markov framework to achieve covariate balance and consistent estimators without model correctness assumptions.
citing papers explorer
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Adaptive Generalized Elliptical Slice Sampling
Adaptively learning the auxiliary ellipse distribution in elliptical slice sampling gives a gradient-free sampler that is ergodic under stated assumptions and empirically competitive with HMC and adaptive random walks on challenging posteriors.
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Error bounds for simultaneous Wasserstein contractive adaptive increasingly rare MCMC
Explicit MSE bounds derived for time-average estimators in adaptive increasingly rare MCMC under simultaneous Wasserstein contraction.
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CBARA: Covariate-Balanced-and-Adjusted Response-Adaptive Randomization
CBARA integrates response-adaptive and covariate-adaptive randomization via a new imbalance vector and pseudo-Markov framework to achieve covariate balance and consistent estimators without model correctness assumptions.