Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
Authors: Hideaki Kim, Taichi Asami, Hiroyuki Toda
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {hideaki.kin.cn, taichi.asami.ka}@hco.ntt.co.jp, hirotoda@acm.org |
| 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. |