Dynamic Programming Based Gaussian Mixture Modelling Tool for 1D and 2D Data
Log-Likelihood for 2D Gaussian Mixture Model
2D plot support
Diagonal initialization of 2D GMM
Dynamic programming initialization of 2D GMM EM
Dynamic programming split of aux
Dynamic programming split
Ellipses for plot 2D GMM
Expectation-maximization algorithm for 2D data
Expectation-maximization algorithm for 1D data
Class assignment for 2D Gaussian Mixture Model data
Thresholds estimations for 1D from GMM distribution
Thresholds estimations for 1D from GMM parameters
Gaussian mixture decomposition for 2D data
Gaussian mixture decomposition for 1D data
Generation of GMM data with high precision
Generator of multiple random 2D mixed-normal distributions
Generator of 1D mixed-normal distributions
Generator of 2D mixed-normal distributions
Default configuration for 1D Gaussian Mixture decomposition
Default configuration for 2D Gaussian Mixture decomposition
Merging of overlapping components
2D plot support
Supporting function for spliters
Probability distribution of 2D normal distribution
Plot of GMM decomposition for 1D data
Plot of GMM decomposition for 2D binned data
Plot of GMM decomposition for 2D data
QQplot of GMM decomposition for 1D data
Random initialization of 2D GMM EM
Function to fit Gaussian Mixture Model (GMM) to 1D data
Function to fit Gaussian Mixture Model (GMM) to 2D data
Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting.