Elimination Ordering in Lifted First-Order Probabilistic Inference
Authors: Seyed Mehran Kazemi, David Poole
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate and compare our proposed relational heuristics, which are Min Table Size, population order and relational min-fill, and also the non-relational min-fill heuristic, which is one of the best known heuristics for nonrelational models, based on a series of empirical and theoretical criteria. Experiments were carried out using C++ with Microsoft Visual Studio 2010 on a 2.63GHz core with 2GB of memory under Windows 7. To evaluate how efficient each heuristic works, we generated parfactor graphs as follows. First, we generated a random number of PRVs having variables with random populations, and then generated a random number of parfactors. Then, we randomly assigned some of the PRVs to each parfactor. Results are presented in Table 1 (the minimum time is in bold). Running time of lifted inference using each of the heuristics are reported in scale of seconds. |
| Researcher Affiliation | Academia | Seyed Mehran Kazemi and David Poole The University of British Columbia Vancouver, BC, V6T 1Z4 {smkazemi, poole}@cs.ubc.ca |
| Pseudocode | Yes | Algorithm 1 Min Table Size algorithm |
| Open Source Code | No | The paper does not contain an unambiguous statement about releasing the source code or a direct link to a code repository. |
| Open Datasets | No | To evaluate how efficient each heuristic works, we generated parfactor graphs as follows. First, we generated a random number of PRVs having variables with random populations, and then generated a random number of parfactors. Then, we randomly assigned some of the PRVs to each parfactor. Since all elements of the parfactor graphs that distinguish one graph from another are generated randomly, the whole space of parfactor graphs can be covered in this way and our generated graphs are arguably reasonable representatives to base the experiments on them. |
| Dataset Splits | No | The paper describes generating synthetic parfactor graphs for evaluation but does not specify any training, validation, or test dataset splits. The generated data is used directly for evaluation of the heuristics. |
| Hardware Specification | Yes | Experiments were carried out using C++ with Microsoft Visual Studio 2010 on a 2.63GHz core with 2GB of memory under Windows 7. |
| Software Dependencies | Yes | Experiments were carried out using C++ with Microsoft Visual Studio 2010 |
| Experiment Setup | No | The paper describes the algorithms and heuristics being evaluated and how parfactor graphs were generated for evaluation, but it does not provide specific experimental setup details such as hyperparameters for the inference algorithms or specific settings beyond the elimination ordering. It mentions: 'We set the maximum allowable time for inference to be five minutes (300 seconds).' but this is a general constraint, not a detailed configuration for the algorithm itself. |