Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
Authors: Hideaki Kim, Taichi Asami, Hiroyuki Toda
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on synthetic and real-world data, and show that it outperforms state-of-the-art methods in terms of predictive accuracy while being substantially faster than a conventional Bayesian method. |
| Researcher Affiliation | Collaboration | Hideaki Kim Taichi Asami Hiroyuki Toda NTT Human Informatics Laboratories NTT Corporation EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are provided at https://github.com/Hid Kim/APP. |
| Open Datasets | Yes | real-world spatial (T R2) data provided by spatstat.data in R (GPL-3) [4]: copper consists of 67 location points of copper ore deposits and 146 line segments representing faults. The covariate of interest is the shortest distance from a given location t to the set of faults (Y R); bei consists of locations of 3605 trees of the species Beilschmiedia pendula and geo-information in a tropical rain forest. The covariates of interest are the terrain elevation and the terrain slope (Y R2); clmfires consists of locations of forest fires in the Castilla-La Mancha region of Spain and the geographical information. |
| Dataset Splits | Yes | For each data set, we randomly split the data points into 10 subsets, assigned one to test and the others to training data, and conducted 10-fold cross validations of the predictive performances... |
| Hardware Specification | Yes | A Mac Book Pro with 4-core CPU (2.8 GHz Intel Core i7) was used. |
| Software Dependencies | No | The paper mentions software like 'spatstat in R' and 'INLA in R' but does not specify their version numbers or the version of R itself, nor does it list other software dependencies with version information. |
| Experiment Setup | Yes | We applied to the APPs a multiplicative Gaussian kernel, k(y, y ) = θ0 QDy d=1 e (θd(yd y d))2, where the hyper-parameter θ = (θ0, . . . , θDy) was optimized for each data by maximizing the marginal likelihood (23) through the 25-points grid search. |