Exact Bernoulli Scan Statistics using Binary Decision Diagrams
Authors: Masakazu Ishihata, Takanori Maehara
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments to evaluate the performance of the proposed algorithm using real-world datasets. |
| Researcher Affiliation | Collaboration | 1NTT Communication Science Laboratories 2RIKEN Center for Advanced Intelligence Project |
| Pseudocode | Yes | Algorithm 1 Construct the weight BDD B; Algorithm 2 Compute the probability P(Wk) on B |
| Open Source Code | No | The paper only mentions using third-party libraries (SAPPOROBDD and Td Zdd) and provides links to their GitHub repositories, not the authors' specific implementation code for the proposed methodology. |
| Open Datasets | Yes | We apply our algorithm to test the locality of real-world observations: the population, income, and GDP changes of US and Japan, and the result of the 2016 US presidential election... We obtained the estimated amounts of population, income, and GDP by state/prefecture from American Fact Finder 5 and e-Stat 6, official portal sites of US and Japanese governmental statistics |
| Dataset Splits | No | The paper describes a statistical test for computing p-values and does not involve machine learning model training, thus typical dataset splits for training, validation, and testing are not applicable or specified. |
| Hardware Specification | Yes | All experiments were conducted on 64-bit Ubuntu 18.04.2 LTS with an Intel Core i7-7700K 3.6 GHz CPU and 16 GB RAM. |
| Software Dependencies | Yes | All code was implemented in C/C++ (gcc 7.3.0 with the -O3 option) using SAPPOROBDD library3 and Td Zdd library4. |
| Experiment Setup | Yes | For each ℓ {2, . . . , |V | 1}, we observed X such that |X| = ℓ and its scan statistics K was also ℓ, that is, all states (or prefectures) with value 1 were connected. Then, we computed the p-value of the above observation X, where we set pi as the empirical probability ℓ/|V | for each i V. |