A hybrid sampler for Poisson-Kingman mixture models
Authors: Maria Lomeli, Stefano Favaro, Yee Whye Teh
NeurIPS 2015 | Venue 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 EMAIL Stefano Favaro Department of Economics and Statistics University of Torino and Collegio Carlo Alberto EMAIL Yee Whye Teh Department of Statistics University of Oxford EMAIL |
| 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. |