GemBag: Group Estimation of Multiple Bayesian Graphical Models
Authors: Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive simulation studies and an application to a bike sharing data set, we demonstrate that the proposed Gem Bag procedure has strong empirical performance in comparison with alternative methods. |
| Researcher Affiliation | Collaboration | Xinming Yang EMAIL Department of Statistics University of Illinois at Urbana-Champaign Champaign, IL, USA Lingrui Gan EMAIL Facebook Menlo Park, CA, USA Naveen N. Narisetty EMAIL Department of Statistics University of Illinois at Urbana-Champaign Champaign, IL, USA Feng Liang EMAIL Department of Statistics University of Illinois at Urbana-Champaign Champaign, IL, USA |
| Pseudocode | Yes | Algorithm 1 EM algorithm for computing the MAP estimator in (12) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It provides a link to the journal page for the paper and its license, but no specific code repository or explicit code release statement. |
| Open Datasets | Yes | We use the Capital Bikeshare trip data1 to evaluate the performance of Gem Bag. The data contains records of bike rentals by either a registered rider or a casual rider in a bicycle sharing system with more than 500 stations. ... 1. Data available at https://www.capitalbikeshare.com/system-data. |
| Dataset Splits | Yes | For each class, we use the first 80 percent observations as training data and the rest 20 percent as test data. |
| Hardware Specification | Yes | We report the computational time of Gem Bag along with the competing methods using a Mac Book Pro with 2.9 GHz Intel Core i5 processor and 8.00 GB memory in Table 5. |
| Software Dependencies | No | The paper mentions 'Map built using the R package ggmap (Kahle and Wickham, 2013)' but does not provide specific version numbers for R or the ggmap package, nor for any other key software components used for the main models or experiments. |
| Experiment Setup | Yes | For Gem Bag, we consider α 1, ?n, and n and estimate the sparsity patterns using a threshold t 0.5 on the posterior inclusion probabilities. We set p1 p2 ? 0.5 so that the prior inclusion probability Pprk,ij 1q p1p2 0.5 and tune pv0, v1q with v0 τ p0.25, 0.5, 0.75, 1q ˆ a 1{pn log pq and v1 p2.5, 5, 7.5, 10q ˆ a 1{pn log pq when α 1, with v0 τ p1, 1.5, 2, 2.5, 3qˆ10 2ˆ a 1{log p and v1 p2, 4, 6, 8qˆ a 1{log p when α ?n, or with v0 τ p1, 1.5, 2, 2.5, 3qˆ10 3ˆ a n{log p and v1 p2, 4, 6, 8qˆ a n{log p when α n. |