Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy
Authors: Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the proposed goodness-of-fit test to three statistical models involving discrete distributions, and our experiments show that the proposed test typically outperforms a two-sample test based on the maximum mean discrepancy. |
| Researcher Affiliation | Academia | 1Department of Statistics, Purdue University, West Lafayette, IN 2Department of Computer Science, The University of Texas at Austin, Austin, TX 3Department of Computer Science, Purdue University, West Lafayette, IN. |
| Pseudocode | Yes | Algorithm 1 Goodness-of-fit testing via KDSD |
| Open Source Code | No | The paper does not explicitly state that its source code for the methodology is released or provide a link to it. |
| Open Datasets | No | The paper describes generating samples from models (Ising, Bernoulli RBM, ERGM) and mentions using 'ergm R package (Handcock et al., 2017)' for ERGM, but does not specify a publicly available or open dataset that is used for training. |
| Dataset Splits | No | The paper describes drawing samples for hypothesis testing (n samples from q for KDSD, and n from q and n from p for MMD) but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | Yes | We utilize the ergm R package (Handcock et al., 2017). R package version 3.8.0. |
| Experiment Setup | Yes | We set m = 5000 for both methods throughout. ... significance level α = 0.05. ... We consider a periodic 10-by-10 lattice, with d = 100 random variables. We focus on the ferromagnetic setting and set θij = 1/T, where T is the temperature of the system. ... We use M = 50 visible units and K = 25 hidden units. We draw the entries of the weight matrix W i.i.d. from a Normal distribution with mean zero and standard deviation 1/M, and the entries of the bias terms b and c i.i.d. from the standard Normal distribution. ... We consider an ERGM distribution for undirected graphs on 20 nodes, with the dimension of each sample d = 20 2 = 190. We fix θ1 = 2 and τ = 0.01. |