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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | pHMC_0.1.0.tar.gz |
69.5 KiB |
0.1.0 |
rolling linux/noble R-4.5 | pHMC_0.1.0.tar.gz |
69.4 KiB |
0.1.0 |
rolling source/ R- | pHMC_0.1.0.tar.gz |
14.5 KiB |
0.1.0 |
latest linux/jammy R-4.5 | pHMC_0.1.0.tar.gz |
69.5 KiB |
0.1.0 |
latest linux/noble R-4.5 | pHMC_0.1.0.tar.gz |
69.4 KiB |
0.1.0 |
latest source/ R- | pHMC_0.1.0.tar.gz |
14.5 KiB |
0.1.0 |
2026-04-23 source/ R- | pHMC_0.1.0.tar.gz |
0 B |