journal

Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Anoop, CV and Negi, Neeraj and Aprem, Anup

Expert Systems with Applications · Vol. 261 · pp. 125475 · 2025

Reference

Cite this work

Anoop, CV and Negi, Neeraj and Aprem, Anup (2025). Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory. Expert Systems with Applications, 261, 125475

Other citation formats

IEEE

Anoop, CV and Negi, Neeraj and Aprem, Anup, “Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory,” Expert Systems with Applications, vol. 261, pp. 125475, 2025.

MLA

Anoop, CV and Negi, Neeraj and Aprem, Anup. “Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory.” Expert Systems with Applications, vol. 261, 2025, pp. 125475.

Chicago

BibTeX

@article{bayesian-machine-learning-framework-for-characterizing-structural-dependency-dynamics-and-volatility-of-cryptocurrency-market-using-potential-field-theory-2025,
	title = {Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory},
	author = {Anoop, CV and Negi, Neeraj and Aprem, Anup},
	journal = {Expert Systems with Applications},
	volume = {261},
	pages = {125475},
	year = {2025}
}