AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms
Authors: Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
JAIR 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified versions of) PC-like algorithm. |
| Researcher Affiliation | Academia | Mohammad Ali Javidian EMAIL Marco Valtorta EMAIL Pooyan Jamshidi EMAIL Department of Computer Science and Engineering University of South Carolina, Columbia, SC, 29201, USA. |
| Pseudocode | Yes | Algorithm 1: Test for minimal separation (Problem 1) ... Algorithm 6: LCD-AMP : A decomposition-based recovery algorithm for AMP CGs |
| Open Source Code | Yes | Code for reproducing our results is available at https://github.com/majavid/AMPCGs2019. |
| Open Datasets | Yes | Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP... We perform simulation studies for four well-known Bayesian networks from Bayesian Network Repository (Scutari, 2017): ASIA, INSURANCE, ALARM, and HAILFINDER. |
| Dataset Splits | No | The paper describes generating i.i.d. samples of size n {500, 1000, 5000, 10000} for random AMP CGs and using 'observations' for discrete Bayesian networks. While sample sizes are given, there are no explicit details on training, validation, or testing splits. |
| Hardware Specification | No | The paper mentions parallel computations and distributing tasks over different cores, but it does not specify any particular CPU or GPU models, or other detailed hardware specifications for the experiments. |
| Software Dependencies | No | The paper mentions 'gaussCItest() function from the R package pcalg' and 'bnlearn R package (Scutari, 2017)'. While R packages are named, specific version numbers for these packages or for R itself are not provided. |
| Experiment Setup | Yes | In our simulation, we change three parameters p (the number of vertices), n (sample size) and N (expected number of adjacent vertices) as follows: p {10, 20, 30, 40, 50}, n {500, 1000, 5000, 10000}, and N {2, 3}. For each sample, three different significance levels (α = 0.005, 0.01, 0.05) are used to perform the hypothesis tests. |