Particle-Gibbs Sampling for Bayesian Feature Allocation Models
Authors: Alexandre Bouchard-Côté, Andrew Roth
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of our proposed methods to the standard Gibbs sampler using synthetic and real data from a range of feature allocation models. Our results suggest that row wise updates using the PG methodology can significantly improve the performance of samplers for feature allocation models. |
| Researcher Affiliation | Academia | Alexandre Bouchard-Côt e EMAIL Department of Statistics, University of British Columbia. Andrew Roth EMAIL Department of Computer Science, University of British Columbia |
| Pseudocode | Yes | Algorithm 1 Sample a row of the feature allocation using the element wise Gibbs update. Algorithm 2 Sample a row of the feature allocation using the row wise Gibbs update. Algorithm 3 Sample a row of the feature allocation using the particle Gibbs update. Algorithm 4 Conditional resampling for DPF. Algorithm 5 Sample a row of the feature allocation using the discrete particle filter update. |
| Open Source Code | Yes | Code implementing the samplers and models is available online at https://github. com/aroth85/pgfa. All experiments were done using version 0.3.4 of the software. Code for performing the experiments is available online at https://github.com/aroth85/pgfa_ experiments. |
| Open Datasets | Yes | We downloaded the Yale Face data from http://vision.ucsd.edu/datasets/yale_face_ dataset_original/yalefaces.zip. |
| Dataset Splits | Yes | We randomly assigned 10% of the data matrix to be missing and used these entries to compute root mean square reconstruction error (RMSE). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., CPU, GPU models, memory, or cloud instances) for running the experiments. It only states that 'All experiments were done using version 0.3.4 of the software.' |
| Software Dependencies | Yes | All experiments were done using version 0.3.4 of the software. |
| Experiment Setup | Yes | Based on these results we used the following parameter values for subsequent experiments. Annealing power: 1.0, Number of particles: 20, Resample threshold: 0.5, Particle Gibbs resampling scheme: Multinomial, Test path: Zeros. |