Maximum likelihood pixel labeling using a spatially variant finite mixture model

1997 ◽  
Vol 44 (4) ◽  
pp. 1578-1582 ◽  
Author(s):  
S. Sanjay Gopal ◽  
T.J. Hebert
Keyword(s):  
Maximum Likelihood ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Spatially Variant ◽  
Pixel Labeling

Finite Mixture Model: A Comparison of Maximum Likelihood Estimation and Bayesian Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 63-63
Author(s):  
Seuk Yen Phoong ◽  
Seuk Wai Phoong
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mixture Model ◽  
Likelihood Estimation ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Skewed Distribution ◽  
Estimation Methods ◽  
Series Data ◽  
Number Of Components

The mixture model is known as model-based clustering that is used to model a mixture of unknown distributions. The clustering of mixture model is based on four important criteria, including the number of components in the mixture model, clustering kernel (such as Gaussian mixture models, Dirichlet, etc.), estimation methods, and dimensionality (Lai et al., 2019). Finite mixture model is a finite dimensional of a hierarchical model. It is useful in modeling the data with outliers, non-normal distributed or heavy tails. Furthermore, finite mixture model is flexible when fitted with the models that have multiple modes or skewed distribution. The flexibility depends on the increasing number of parameters with the existence of a number of components. The finite mixture model is a flexible model family and widely applied for large heterogeneous datasets. In addition, the finite mixture model is a probabilistic model that is used to examine the presence of unobserved situations or groups and to measure the distinct parameters or distribution. The situations, such as trend, seasoning, crisis time, normal situation, etc., might affect the number of components that exist for a probabilistic distribution. Furthermore, the finite mixture model is essential for time series data because these data exhibit nonlinearity properties and may have missing data or a jump-diffusion situation (Gensler, 2017; McLachlan and Lee, 2019). Keywords: Bayesian method; Finite Mixture Model; Maximum Likelihood Estimation; Prior distribution; Likelihood Function.

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