A Birth-Death Process for Feature Allocation
Authors: Konstantina Palla, David Knowles, Zoubin Ghahramani
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments We experimentally evaluate the BEP model on real-world genomics and social network data. To evaluate the model fit, we compared the BEP model to independent models at each time point. |
| Researcher Affiliation | Collaboration | 1University of Oxford, Oxford, UK 2Stanford University, California, USA 3University of Cambridge, Cambridge, UK 4Uber AI Labs, SF, California, USA. |
| Pseudocode | No | The paper describes the model and processes mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or provide links to a code repository. |
| Open Datasets | Yes | Here we used a subset of the gene expression data from Piechota et al. (2010)..., For this experiment we used Ch IP-seq (chromatin immunoprecipitation sequencing) data downloaded from the ENCODE project (Consortium, 2007), In van de Bunt et al. (1999), 32 university freshman students... |
| Dataset Splits | Yes | We created 7 train-test splits holding out 20% of the data... and We ran 7 different held-out tests, holding out a different 20% of the data each time. and holding out 10% of all links across all time points. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions employing Markov Chain Monte Carlo (MCMC) for posterior inference but does not provide specific software names with version numbers for implementation or dependencies. |
| Experiment Setup | Yes | We created 7 train-test splits holding out 20% of the data, and ran 700 MCMC iterations. and a burnin of 500. and We choose a Gaussian prior over A, i.e Afm N(0, 1). and we assume the priors wt(k, l) N µw, σ2 w and s N µs, σ2 s . |