Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Authors: Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches. |
| Researcher Affiliation | Collaboration | 1NEC Corporation 2National Institute of Advanced Industrial Science and Technology 3Max Planck Institute for Intelligent Systems 4The Institute of Statistical Mathematics. |
| Pseudocode | Yes | Algorithm 1 Kernel Recursive ABC |
| Open Source Code | No | The paper mentions using 'publicly available code3' for comparison with a third-party method (Bayesian Optimization), but does not state that the code for their proposed Kernel Recursive ABC method is open-source or publicly available. |
| Open Datasets | Yes | Crowd Walk, a publicly available real-world simulator5 for the movements of pedestrians in a commercial district (Yamashita et al., 2010). Footnote 5: https://github.com/crest-cassia/CrowdWalk |
| Dataset Splits | Yes | That is, to evaluate one configuration of hyper-parameters, we first used 75% of the observed data for point estimation and then computed the discrepancy between the rest of the observed data and the ones simulated from point estimates |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'GPyOpt' (for Bayesian optimization) but does not provide specific version numbers for any software dependencies used in its experiments. |
| Experiment Setup | Yes | The bandwidth of a Gaussian kernel was selected from candidate values, each of which is the median (of pairwise distances) multiplied by logarithmically equally spaced values between 2 4 and 24 (Takeuchi et al., 2006, Sec. 5.1.1). Regularization constants for the proposed method and kernel ABC, as well as the soft threshold for K2-ABC, were selected from logarithmically spaced values between 10 4 and 1. |