Slice Sampling on Hamiltonian Trajectories

Authors: Benjamin Bloem-Reddy, John Cunningham

ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In order to test the effectiveness and flexibility of HSS, we performed experiments on two very different models.
Researcher Affiliation Academia Department of Statistics, Columbia University
Pseudocode Yes Algorithm 1 Hamiltonian slice sampling
Open Source Code No The paper does not provide any explicit statements about the release of open-source code for the methodology or links to a code repository.
Open Datasets Yes using the galaxy dataset, with n = 82 observations, that was also used in Favaro & Teh (2013); Lomeli et al. (2015).
Dataset Splits No The paper mentions using synthetic and 'galaxy' datasets but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup) required for reproduction.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments.
Software Dependencies No The paper mentions using the 'R-CODA package' but does not provide specific version numbers for it or any other key software dependencies.
Experiment Setup Yes Slice sampling parameters for HSS were set at w = 0.5 and m = 8. Momentum variables were sampled i.i.d. pi N(0, σ2 p), with σp {0.1, 0.25}.