On the Estimation of Gaussian Mixture Copula Models
Authors: Ashutosh Tewari
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The main claims of this work are supported by empirical evidence gathered on synthetic and realworld datasets. and Section 6. Experimental Results This section provides empirical evidence to support the claims made in this paper using synthetic and real-world datasets. |
| Researcher Affiliation | Industry | 1Amazon Mechatronics, Seattle, USA. Correspondence to: Ashutosh Tewari <ashutosh80@gmail.com>. |
| Pseudocode | No | The paper describes algorithmic procedures (e.g., EM algorithm, numerical schemes for quantile computation) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks with structured steps. |
| Open Source Code | Yes | The contributed GMCM code (Tewari, 2023) employs the parallel implementation of Chandrupatla s algorithm via the Tensor Flow Probability package. and Code used in this analysis is available here (Tewari, 2023). |
| Open Datasets | Yes | Finally, GMCM is learned on several density estimation benchmarks from the UCI repository, after following the pre-processing step described in (Papamakarios et al., 2017). |
| Dataset Splits | Yes | This number was ascertained by a grid search over {10,20,30,40,50} and tracking the likelihood of validation datasets. |
| Hardware Specification | No | The paper mentions 'CPU time' in relation to computational complexity but does not specify any particular CPU model, GPU, or other hardware specifications used for the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Python' and the 'Tensor Flow-Probability package' but does not specify their version numbers or other key software dependencies with specific versions. |
| Experiment Setup | Yes | The maximization is carried out using the Adam optimizer (Kingma & Ba, 2017) with the default learning rate of 1E 3. and the mixture models (GMCM and GMM) were instantiated with the 40 mixing components. |