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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Probabilistic Path Hamiltonian Monte Carlo

ICML 2017 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Stochastic gradient MCMC · 30 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add surrogate smoothing (delta ~ 2*epsilon) to the potential near topology boundaries (ablation)PPHMC on DS4 phylogenetic posterior · DS4largest usable step size at acceptance 0.65: step size nearly 10 times greater than exact PPHMCHelped
add surrogate smoothing to the potential (ablation)PPHMC on DS4 long trajectories · DS4acceptance probability and path length (NNI moves)Helped
sample with PPHMC (surrogate-free posterior sampling on orthant complex)PPHMC on simulated phylogenetic data · Simulated data N=50correctness of posterior sampleNo clear effect

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