REVIEW 1 cited by
The Geometry of Hamiltonian Monte Carlo
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
With its systematic exploration of probability distributions, Hamiltonian Monte Carlo is a potent Markov Chain Monte Carlo technique; it is an approach, however, ultimately contingent on the choice of a suitable Hamiltonian function. By examining both the symplectic geometry underlying Hamiltonian dynamics and the requirements of Markov Chain Monte Carlo, we construct the general form of admissible Hamiltonians and propose a particular choice with potential application in Bayesian inference.
Forward citations
Cited by 1 Pith paper
-
State Space Model Programming in Turing.jl
SSMProblems.jl and GeneralisedFilters.jl provide a unified Julia interface for defining state space models and running Kalman, particle, and Rao-Blackwellised filter inference with GPU support.
Discussion (0). Continue with ORCID to comment.