Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang3664-3671
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We generated 100 random UCCGs of order p = 20, , 60 with r p 2 as the number of edges based on the procedure proposed in (He, Jia, and Yu 2015), where parameter r controls the graph density. We compared the proposed algorithm with the counting algorithm in (He, Jia, and Yu 2015) in Table 1. Figure 3 depicts SDNE versus the number of samples. |
| Researcher Affiliation | Academia | 1Department of ECE, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2Electrical Engineering Department, Sharif University of Technology, Tehran, Iran 3Departments of ECE and ISE, University of Illinois at Urbana-Champaign, Urbana, IL, USA 4Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1 MEC Size Calculator, Algorithm 2 Uniform Sampler |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | We generated 100 random UCCGs of order p = 10, 20, 30, with r p 2 edges, where r = 0.2. The paper does not state that this generated data is publicly available. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits for its own experimental setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for its experiments. |
| Experiment Setup | No | The paper describes the generation of random graphs for simulation, but it does not specify any experimental setup details such as hyperparameters, optimization settings, or model initialization for a learning task. |