A Kernel Test of Goodness of Fit
Authors: Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our test to quantifying convergence of approximate Markov Chain Monte Carlo methods, statistical model criticism, and evaluating quality of fit vs model complexity in nonparametric density estimation.Section 4 contains experimental illustrations on synthetic examples, statistical model criticism, bias-variance trade-offs in approximate MCMC, and convergence in nonparametric density estimation. |
| Researcher Affiliation | Academia | Gatsby Unit, University College London, United Kingdom |
| Pseudocode | No | The paper describes the test procedure in a list of steps in Section 2.2, but this is presented as plain text and not formatted as pseudocode or an algorithm block. |
| Open Source Code | Yes | Code can be found at https://github.com/karlnapf/kernel_goodness_of_fit. |
| Open Datasets | No | The paper mentions using the solar dataset but does not provide a direct link, DOI, or a citation with author names and year for public access. |
| Dataset Splits | No | The paper describes training and testing data splits (e.g., We fit Ntrain = 361 data..., ...remaining Ntest = 41 data) but does not explicitly mention a validation data split or its size. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for experiments, such as GPU/CPU models, memory specifications, or cloud computing instance types. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8, CUDA 11.1). |
| Experiment Setup | Yes | For a fixed number of degrees of freedom we drew 1400 samples and calculated the p-value. This procedure was repeated one hundred times...We emphasize the need for an appropriate choice of the wild bootstrap process parameter, an, which indicates the probability of a sign flip. In Figure 1 we plot p-values for an being set to 0.5. ... We have thinned the observations by a factor of 20 and set an = 0.1...Sample size was set to 500/1000, a_n=0.5. |