pHMC: Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian
Inference
Implements the Proximal Hamiltonian Monte Carlo (p-HMC)
algorithm for Bayesian sampling and estimation from non-differentiable
target densities. The method decomposes a target potential into a smooth
component f(x) and a non-smooth convex component g(x), approximating
only g(x) via its Moreau-Yosida envelope while retaining exact gradient
information for f(x). This approach, based on the methodology described
in Shukla, Vats, and Chi (2025) <doi:10.48550/arXiv.2510.22252>,
yields improved Hamiltonian conservation over full-potential smoothing
approaches. The package provides generalized routines accepting
user-defined probability density functions, log-likelihoods, priors,
and proximal operators, together with automated hyperparameter tuning
for the Moreau-Yosida regularization parameter, Markov chain Monte
Carlo convergence diagnostics, effective sample size computation,
and model evaluation metrics including the Akaike information criterion
and Bayesian information criterion.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=pHMC
to link to this page.
[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]