This paper is in the following e-collection/theme issue:

AI for Synthetic Data (3) Artificial Intelligence (1958) Foundation Models and Their Applications in AI (46) Generative Language Models Including ChatGPT (613) AI Data (18)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65729, first published .
Utility-based Analysis of Statistical Approaches and Deep Learning Models for Synthetic Data Generation With Focus on Correlation Structures: Algorithm Development and Validation

Utility-based Analysis of Statistical Approaches and Deep Learning Models for Synthetic Data Generation With Focus on Correlation Structures: Algorithm Development and Validation

Utility-based Analysis of Statistical Approaches and Deep Learning Models for Synthetic Data Generation With Focus on Correlation Structures: Algorithm Development and Validation

Authors of this article:

Marko Miletic1 Author Orcid Image ;   Murat Sariyar1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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  2. Fasseeh A, Ashmawy R, Hren R, ElFass K, Imre A, Németh B, Nagy D, Nagy B, Vokó Z. Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints. Algorithms 2025;18(8):475 View
  3. Foung D, Kohnke L. Generating synthetic data for CALL research with GenAI: A proof-of-concept study. Research Methods in Applied Linguistics 2025;4(3):100248 View