NUTS-mul and NUTS-BPS show nearly identical qualitative ergodicity behavior depending on target tails, with both mixing in O(d^{1/4}) time for Gaussians but smaller constants for NUTS-BPS.
Adaptive stereographic mcmc
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Introduces lowBM3, the first rank-based Bayesian mixture model for joint unsupervised clustering and variable selection in ultra-high-dimensional data, with simulations and application to breast cancer RNA-seq.
An adaptive hierarchical RMHMC sampler with closed-form leapfrog integrator and automatic mass matrix tuning for efficient MCMC in high-dimensional Bayesian problems.
Explicit MSE bounds derived for time-average estimators in adaptive increasingly rare MCMC under simultaneous Wasserstein contraction.
citing papers explorer
-
A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets
NUTS-mul and NUTS-BPS show nearly identical qualitative ergodicity behavior depending on target tails, with both mixing in O(d^{1/4}) time for Gaussians but smaller constants for NUTS-BPS.
-
Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures
Introduces lowBM3, the first rank-based Bayesian mixture model for joint unsupervised clustering and variable selection in ultra-high-dimensional data, with simulations and application to breast cancer RNA-seq.
-
Adaptive Riemannian Manifold Hamiltonian Monte Carlo with Hierarchical Metric
An adaptive hierarchical RMHMC sampler with closed-form leapfrog integrator and automatic mass matrix tuning for efficient MCMC in high-dimensional Bayesian problems.
-
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.