Log-density gradient covariance and automatic metric tensors for Riemann manifold Monte Carlo methods

DOI10.1111/sjos.12705MaRDI QIDQ6608190

Tore Selland Kleppe

Publication date: 19 September 2024

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

zbMATH Keywords

metric tensorMCMCgeneralized randomized Hamiltonian Monte CarloRiemann manifold Monte Carlo

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On leverage in a stochastic volatility model
• Piecewise deterministic Markov processes for continuous-time Monte Carlo
• Randomized Hamiltonian Monte Carlo
• Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Solving Ordinary Differential Equations I
• Simulating Hamiltonian Dynamics
• Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models
• Direct Methods for Sparse Linear Systems
• Numerical Optimization
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Gaussian Markov Random Fields
• Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates
• Geometric integrators and the Hamiltonian Monte Carlo method
• A General Metric for Riemannian Manifold Hamiltonian Monte Carlo
• Factor Multivariate Stochastic Volatility via Wishart Processes
• The Gibbs sampler with particle efficient importance sampling for state-space models*

Related Items (1)

Tuning diagonal scale matrices for HMC

This page was built for publication: Log-density gradient covariance and automatic metric tensors for Riemann manifold Monte Carlo methods