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Geometric ergodicity of trans-dimensional Markov chain Monte Carlo algorithms
Q Qin - Journal of the American Statistical Association, 2024 - Taylor & Francis
This article studies the convergence properties of trans-dimensional MCMC algorithms
when the total number of models is finite. It is shown that, for reversible and some …
when the total number of models is finite. It is shown that, for reversible and some …
Spectral gap bounds for reversible hybrid Gibbs chains
Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that
utilize Markov chains to approximate conditional distributions, with the Metropolis-within …
utilize Markov chains to approximate conditional distributions, with the Metropolis-within …
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 …
The No-Underrun Sampler: A Locally-Adaptive, Gradient-Free MCMC Method
In this work, we introduce the No-Underrun Sampler (NURS): a locally-adaptive, gradient-
free Markov chain Monte Carlo method that combines elements of Hit-and-Run and the No …
free Markov chain Monte Carlo method that combines elements of Hit-and-Run and the No …
Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC
We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA)
estimation and signal detection. Unlike previous works in wideband DoA estimation and …
estimation and signal detection. Unlike previous works in wideband DoA estimation and …
Quantile Slice Sampling
MJ Heiner, SB Johnson, JR Christensen… - arxiv preprint arxiv …, 2024 - arxiv.org
We propose and demonstrate an alternate, effective approach to simple slice sampling.
Using the probability integral transform, we first generalize Neal's shrinkage algorithm …
Using the probability integral transform, we first generalize Neal's shrinkage algorithm …
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 …
A dimension-independent bound on the Wasserstein contraction rate of a geodesic random walk on the sphere
P Schär, TD Stier - Electronic Communications in Probability, 2024 - projecteuclid.org
We theoretically analyze the properties of a geodesic random walk on the Euclidean d-
sphere. Specifically, we prove that the random walk's transition kernel is Wasserstein …
sphere. Specifically, we prove that the random walk's transition kernel is Wasserstein …