Recent Advances in Statistical Mixture Models: Challenges and Applications

Sami Bourouis

2023

Abstract

This paper discusses current advances in mixture models, as well as modern approaches and tools that make use of mixture models. In particular, the contribution of mixture-based modeling in various area of researches is discussed. It exposes many challenging issues, especially the way of selecting the optimal model, estimating the parameters of each component, and so on. Some of newly emerging mixture model-based methods that can be applied successfully are also cited. Moreover, an overview of latest developments as well as open problems and potential research directions are discussed. This study aims to demonstrate that mixture models may be consistently proposed as a powerful tool for carrying out a variety of difficult real-life tasks. This survey can be the starting point for beginners as it allows them to better understand the current state of knowledge and assists them to develop and evaluate their own frameworks.

Paper Citation

in Harvard Style

Bourouis S. (2023). Recent Advances in Statistical Mixture Models: Challenges and Applications. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 312-319. DOI: 10.5220/0011660900003411

in Bibtex Style

@conference{icpram23,
author={Sami Bourouis},
title={Recent Advances in Statistical Mixture Models: Challenges and Applications},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={312-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011660900003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Recent Advances in Statistical Mixture Models: Challenges and Applications
SN - 978-989-758-626-2
AU - Bourouis S.
PY - 2023
SP - 312
EP - 319
DO - 10.5220/0011660900003411