arXiv Machine Learning
Variance Reduction for Independent Metropolis
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arXiv Machine Learning
Jul 14
Gaussian Invariant Markov Chain Monte Carlo
arXiv:2506. 21511v2 Announce Type: replace-cross Abstract: We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA.
arXiv Statistics ML
Aug 28
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection
The paper introduces a two-step Metropolis–Hastings algorithm designed to efficiently sample from Bayesian empirical likelihood (BayesEL) posterior distributions, addressing challenges posed by the complex, often non‑convex support of empirical likelihood. The method leverages current parameter values and estimating equations to propose new values for remaining parameters, making it suitable for problems with discontinuous estimating equations such as simultaneous quantile regression. Additionally, the approach extends naturally to BayesEL model selection via reversible‑jump MCMC, and the authors demonstrate its utility through several real‑life applications.
arXiv Machine Learning
Jun 29
PAC-Bayesian Certificates for Quadratic Closed-Loop Control
arXiv:2606. 28281v1 Announce Type: cross Abstract: PAC-Bayesian bounds provide finite-sample guarantees for data-dependent randomized predictors, but applying them to learning-based control is difficult because the natural objective is a quadratic trajectory cost.
arXiv Machine Learning
Jul 2
Optimal scaling of MCMC algorithms: exploiting the symmetry of the Metropolis-Hastings formula
arXiv:2607. 00586v1 Announce Type: cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases.
arXiv Machine Learning
Jul 14
Optimal scaling of MCMC algorithms: the Hamiltonian approach
arXiv:2607. 00586v2 Announce Type: replace-cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases.
arXiv Machine Learning
Jul 1
Sequential sparse Gaussian process quantile regression
arXiv:2606. 31284v1 Announce Type: new Abstract: Quantile regression aims to estimate the conditional quantiles of a response variable from observed data.