REVIEW 10 cited by
MCMC using Hamiltonian dynamics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious "momentum" variables. A key to its usefulness is that Hamiltonian dynamics preserves volume, and its trajectories can thus be used to define complex mappings without the need to account for a hard-to-compute Jacobian factor - a property that can be exactly maintained even when the dynamics is approximated by discretizing time. In this review, I discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.
Forward citations
Cited by 10 Pith papers
-
A new framework for lightning-fast gravitational wave analysis of pulsar timing data
A standardizing coordinate transform turns PTA Fourier coefficients into near-standard normals so HMC/NUTS on GPU recovers NANOGrav-scale posteriors in ~15 minutes.
-
The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone
A new ray-tracing MCMC sampler keeps ray speed constant, making it far more robust to stochastic gradients and able to sample billion-parameter neural networks on one GPU.
-
Learning Turbulence Closures with Physics-Informed Neural Networks for the Rayleigh-Taylor Transition to Turbulence
A PINN-derived analytical correction to the dissipation-production coefficient C_ε0 in a k-ε-b RANS model captures the Rayleigh-Taylor transition to turbulence by coupling dissipation to the Froude number and directed...
-
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
Even a perfect diffusion model yields poor annealed Boltzmann generators when coupled through first-order stochastic denoising kernels, while deterministic transport maps and second-order kernels improve; with learned...
-
DISCO-DJ II: a differentiable particle-mesh code for cosmology
A GPU-accelerated, differentiable particle-mesh N-body code achieves per-cent-level power-spectrum accuracy with few time steps and recovers sigma_8 plus initial conditions from a noisy mock field.
-
Detectability and Parameter Estimation for Einstein Telescope Configurations with GWJulia
A new open-source Julia tool forecasts Einstein Telescope parameter-estimation accuracy, finding the 2L45 design marginally best for single parameters but comparable to other layouts when joint precision is required.
-
R2 priors for Grouped Variance Decomposition in High-dimensional Regression
A two-stage 'Group-R2' Bayesian prior decomposes explained variance across and within predictor groups, improving shrinkage and predictive performance in distributed-signal settings.
-
Integrating tumor burden with survival outcome for treatment effect evaluation in oncology trials
A Bayesian utility-based endpoint combining tumor burden AUC with survival time to estimate treatment effects in oncology trials, evaluated only on simulations with an internal data-generation mismatch.
-
PolyStan: PolyChord nested sampling and Bayesian evidences for Stan models
PolyStan wraps PolyChord nested sampling for Stan models, enabling evidence computation and multimodal posterior sampling through a cmdstan-style interface.
-
Bayesian integrative factor analysis methods, with application in nutrition and genomics data
A comparative tutorial evaluating five Bayesian integrative factor models plus two baselines on simulated and real multi-study data, with code, showing method performance is scenario-dependent.
Discussion (0). Sign in to comment.