A hybrid sampler for Poisson-Kingman mixture models
Authors: Maria Lomeli, Stefano Favaro, Yee Whye Teh
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe comparative simulation results demonstrating the efficacy of the proposed MCMC algorithm against existing marginal and conditional MCMC samplers. We used the dataset from Roeder [26] to test the algorithmic performance in terms of running time and effective sample size (ESS), as Table 1 shows. |
| Researcher Affiliation | Academia | Mar ıa Lomel ı Gatsby Unit University College London mlomeli@gatsby.ucl.ac.uk Stefano Favaro Department of Economics and Statistics University of Torino and Collegio Carlo Alberto stefano.favaro@unito.it Yee Whye Teh Department of Statistics University of Oxford y.w.teh@stats.ox.ac.uk |
| Pseudocode | Yes | see Algorithm 1 in the supplementary material for details. see Algorithm 2 in the supplementary material for details. see Algorithm 4 in the supplementary material for details. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing their code or provide a link to a code repository. |
| Open Datasets | Yes | We used the dataset from Roeder [26] to test the algorithmic performance in terms of running time and effective sample size (ESS), as Table 1 shows. The dataset consists of measurements of velocities in km/sec of n 82 galaxies from a survey of the Corona Borealis region. |
| Dataset Splits | No | The paper mentions using a dataset for testing but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers that would be needed for replication. |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup, such as specific hyperparameter values, learning rates, or training configurations. |