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Markov chain Monte Carlo without evaluating the target: an auxiliary variable approach
W Yuan, G Wang - arxiv preprint arxiv:2406.05242, 2024 - arxiv.org
In sampling tasks, it is common for target distributions to be known up to a normalising
constant. However, in many situations, evaluating even the unnormalised distribution can be …
constant. However, in many situations, evaluating even the unnormalised distribution can be …
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
We study the complexity of heavy-tailed sampling and present a separation result in terms of
obtaining high-accuracy versus low-accuracy guarantees ie, samplers that require only $ O …
obtaining high-accuracy versus low-accuracy guarantees ie, samplers that require only $ O …
On the large deviation principle for Metropolis-Hastings Markov Chains: the Lyapunov function condition and examples
With an aim to analyse the performance of Markov chain Monte Carlo (MCMC) methods, in
our recent work we derive a large deviation principle (LDP) for the empirical measures of …
our recent work we derive a large deviation principle (LDP) for the empirical measures of …
Alternative representation of the large deviation rate function and hyperparameter tuning schemes for Metropolis-Hastings Markov Chains
F Milinanni - arxiv preprint arxiv:2409.20337, 2024 - arxiv.org
Markov chain Monte Carlo (MCMC) methods are one of the most common classes of
algorithms to sample from a target probability distribution $\pi $. A rising trend in recent …
algorithms to sample from a target probability distribution $\pi $. A rising trend in recent …