mlsanityData to 7 Oct 2026

Tuning diagonal scale matrices for HMC

arXiv 2403.07495 ↗First posted 12 Mar 2024stat.CO stat.ME stat.MLcited by 4

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Abstract (arXiv)

Three approaches for adaptively tuning diagonal scale matrices for HMC are discussed and compared. The common practice of scaling according to estimated marginal standard deviations is taken as a benchmark. Scaling according to the mean log-target gradient (ISG), and a scaling method targeting that the frequency of when the underlying Hamiltonian dynamics crosses the respective medians should be uniform across dimensions, are taken as alternatives. Numerical studies suggest that the ISG method leads in many cases to more efficient sampling than the benchmark, in particular in cases with strong correlations or non-linear dependencies. The ISG method is also easy to implement, computationally cheap and would be relatively simple to include in automatically tuned codes as an alternative to the benchmark practice.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 53 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

13 of 53 references.

References · 13

  1. 2019Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMCcited by 90
  2. 2017Geometric integrators and the Hamiltonian Monte Carlo methodcited by 25
  3. 2016Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlocited by 18
  4. 2021Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlocited by 18
  5. 2023When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?cited by 12
  6. 2015Leave Pima Indians alone: binary regression as a benchmark for Bayesian computationcited by 11
  7. 2020Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Ratescited by 8
  8. 2022Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random stepscited by 8
  9. 2018Dynamically rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Modelscited by 3
  10. 2022Randomized Time Riemannian Manifold Hamiltonian Monte Carlocited by 3
  11. 2022Log-density gradient covariance and automatic metric tensors for Riemann manifold Monte Carlo methodscited by 3
  12. 2016Modified Cholesky Riemann Manifold Hamiltonian Monte Carlo: Exploiting Sparsity for Fast Sampling of High-dimensional Targetscited by 2
1 more
  1. 2020Couplings for Andersen Dynamicscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Variational Inference for Uncertainty Quantification: an Analysis of Trade-offscited by 11
  2. 2025The Within-Orbit Adaptive Leapfrog No-U-Turn Samplercited by 2
  3. 2025Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densitiescited by 0
  4. 2026High-dimensional Adaptive MCMC with Reduced Computational Complexitycited by 0