An Algorithm to Learn Polytree Networks with Hidden Nodes
Authors: Firoozeh Sepehr, Donatello Materassi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this article, we develop an algorithm to exactly recover graphical models of random variables with underlying polytree structures when the latent nodes satisfy specific degree conditions. Therefore, this article proposes an approach for the full identification of hidden variables in a polytree. We also show that the algorithm is complete in the sense that when such degree conditions are not met, there exists another polytree with fewer number of latent nodes satisfying the degree conditions and entailing the same independence relations among the observed variables, making it indistinguishable from the actual polytree. |
| Researcher Affiliation | Academia | Firoozeh Sepehr Department of EECS University of Tennessee Knoxville 1520 Middle Dr, Knoxville, TN 37996 dawn@utk.edu; Donatello Materassi Department of EECS University of Tennessee Knoxville 1520 Middle Dr, Knoxville, TN 37996 dmateras@utk.edu |
| Pseudocode | Yes | Algorithm 1 Hidden Cluster Merging Algorithm; Algorithm 2 Hidden Cluster Learning Algorithm; Algorithm 3 Hidden Root Recovery Algorithm |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets, nor does it specify training procedures or dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not mention any validation dataset splits or processes. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and describes an algorithm without mentioning any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and describes an algorithm, not an experiment. Therefore, no experimental setup details like hyperparameters are provided. |