Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions
From MaRDI portal
Publication:4563129
DOI10.1142/S0219876218500123zbMath1404.65008arXiv1506.07450OpenAlexW2964185649WikidataQ60656248 ScholiaQ60656248MaRDI QIDQ4563129
Monika Pietrowska, Michał Marczyk, Joanna Polanska, Piotr Widlak, Andrzej Polański
Publication date: 6 June 2018
Published in: International Journal of Computational Methods (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1506.07450
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A profile likelihood method for normal mixture with unequal variance
- Measuring the component overlapping in the Gaussian mixture model
- Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Choosing initial values for the EM algorithm for finite mixtures
- Model-based cluster and discriminant analysis with the MIXMOD software
- Estimating the dimension of a model
- Computational aspects of fitting mixture models via the expectation-maximization algorithm
- MCLUST: Software for model-based cluster analysis
- A likelihood-based constrained algorithm for multivariate normal mixture models
- Least Squares Estimators of Peptide Species Concentrations Based on Gaussian Mixture Decompositions of Protein Mass Spectra
- On the Bumpy Road to the Dominant Mode
- Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
- On the approximation of curves by line segments using dynamic programming
This page was built for publication: Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions