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

Nonparametric Hamiltonian Monte Carlo

ICML 2021 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Sequential Monte Carlo for inference · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use NP-DHMC instead of PGibbsGeometric distribution inference · Geometric distributionTVD: 0.0158 -> 0.0136Helped
Use NP-DHMC instead of RMHGeometric distribution inference · Geometric distributionTVD: 0.0196 -> 0.0136Helped
Use NP-DHMC instead of LMHGeometric distribution inference · Geometric distributionTVD: 0.0224 -> 0.0136Helped
Use NP-DHMC for posterior inferenceOne-sided random walk model · Random walkeffective sample sizeHelped
Use NP-DHMC for posterior inferenceGaussian mixture model · Gaussian mixture modelLPPDHelped
Use NP-DHMC for posterior inferenceDirichlet process mixture model · Dirichlet process mixture modelLPPDHelped
Use Pyro HMC or NUTS instead of NP-DHMCOne-sided random walk model · Random walkposterior accuracyHurt
Add trace trimming to the extend procedure (ablation)NP-DHMC · ExperimentsperformanceHelped

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