Asymptotic Accuracy of Distribution-Based Estimation of Latent Variables
Authors: Keisuke Yamazaki
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present paper formulates distribution-based functions for the errors in the estimation of the latent variables. The asymptotic behavior is analyzed for both the maximum likelihood and the Bayes methods. Keywords: unsupervised learning, hierarchical parametric models, latent variable, maximum likelihood method, Bayes method. The goal of the present paper is to provide an error function for measuring the accuracy, which is suitable for the unsupervised learning with hierarchical models, and to derive its asymptotic form. |
| Researcher Affiliation | Academia | Keisuke Yamazaki EMAIL Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology G5-19 4259 Nagatsuta, Midori-ku, Yokohama, Japan |
| Pseudocode | No | The paper describes mathematical derivations and proofs for asymptotic error functions. It does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making source code available, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical analysis of latent variable estimation. It does not describe any experiments that would involve specific datasets, nor does it provide access information for any datasets. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on datasets. Therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper is a theoretical work focusing on mathematical analysis and proofs. It does not describe any computational experiments or hardware used. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations. It does not mention any software or libraries with version numbers used for implementation or analysis. |
| Experiment Setup | No | The paper is theoretical and presents asymptotic analysis of error functions. It does not describe any experimental setup, hyperparameters, or training configurations. |