Adaptive Step Size Selection for Hessian-Based Manifold Langevin Samplers

DOI10.1111/sjos.12204zbMath1468.62435arXiv1501.07454OpenAlexW2963122607MaRDI QIDQ2821480

Tore Selland Kleppe

Publication date: 21 September 2016

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.07454

zbMATH Keywords

Markov chain Monte CarloHamiltonian Monte Carloadaptive step sizemanifold Langevinmodified Cholesky algorithm

Mathematics Subject Classification ID

Statistics on manifolds (62R30) Monte Carlo methods (65C05) Statistical ranking and selection procedures (62F07)

Related Items (5)

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates ⋮ Polya tree Monte Carlo method ⋮ Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets ⋮ Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for Pharmacodynamics ⋮ Geometric adaptive Monte Carlo in random environment

Uses Software

• QRM
• CSparse

Cites Work

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