Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach

2012 ◽  
Vol 82 ◽  
pp. 22-30 ◽  
Author(s):  
Jie Yu
Keyword(s):  
Gaussian Process ◽  
Mixture Model ◽  
Gaussian Process Regression ◽  
Finite Mixture Model ◽  
Chemical Processes ◽  
Finite Mixture ◽  
Quality Prediction ◽  
Model Based ◽  
Regression Approach ◽  
Non Gaussian

scholarly journals Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging

2007 ◽  
Vol 26 (5) ◽  
pp. 696-711 ◽  
Author(s):  
J. Tohka ◽  
E. Krestyannikov ◽  
I.D. Dinov ◽  
A.M. Graham ◽  
D.W. Shattuck ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Based ◽  
Voxel Classification

A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction

2019 ◽  
Vol 85 ◽  
pp. 105789 ◽  
Author(s):  
Yatong Zhou ◽  
Xiangyu Zhao ◽  
Kuo-Ping Lin ◽  
Ching-Hsin Wang ◽  
Lingling Li
Keyword(s):  
Air Quality ◽  
Gaussian Process ◽  
Mixture Model ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Quality Prediction ◽  
Model Based ◽  
Air Quality Prediction

scholarly journals Detecting influential observations in a model-based cluster analysis

2016 ◽  
Vol 27 (2) ◽  
pp. 521-540 ◽  
Author(s):  
Liesbeth Bruckers ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Helena Geys
Keyword(s):  
Mixture Model ◽  
Clustering Algorithms ◽  
Real Life ◽  
Finite Mixture Model ◽  
Repeated Measurements ◽  
Finite Mixture ◽  
Population Heterogeneity ◽  
Complex Data ◽  
Influential Observations ◽  
Model Based

Finite mixture models have been used to model population heterogeneity and to relax distributional assumptions. These models are also convenient tools for clustering and classification of complex data such as, for example, repeated-measurements data. The performance of model-based clustering algorithms is sensitive to influential and outlying observations. Methods for identifying outliers in a finite mixture model have been described in the literature. Approaches to identify influential observations are less common. In this paper, we apply local-influence diagnostics to a finite mixture model with known number of components. The methodology is illustrated on real-life data.

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