A study on the PBL writing class model based on the topic of‘COVID-19’

2021 ◽  
Vol 6 (2) ◽  
pp. 93-121
Author(s):  
Sookyung Lee
Keyword(s):  
Class Model ◽  
Model Based ◽  
Writing Class

Predicting gene phenotype by multi-label multi-class model based on essential functional features

2021 ◽  
Author(s):  
Lei Chen ◽  
Zhandong Li ◽  
Tao Zeng ◽  
Yu-Hang Zhang ◽  
Hao Li ◽  
...  
Keyword(s):  
Class Model ◽  
Model Based ◽  
Functional Features

Applications of Computational and Model-Based Statistical Methodologies in Archaeology

2011 ◽  
pp. 1-26
Author(s):  
Ioulia Papageorgiou
Keyword(s):  
Statistical Analysis ◽  
Latent Class ◽  
Rapid Development ◽  
Latent Class Model ◽  
Class Model ◽  
Normal Distributions ◽  
Model Based Clustering ◽  
Model Based ◽  
Bayesian Mixture ◽  
Mixture Of Normal Distributions

Quantitative Archaeology had a rapid development in the past few decades due to the parallel development of methodologies in Physics, Chemistry and Geology that can be implemented in archaeological findings and produce measurements on a number of variables. Those measurements form the data, the basis for a statistical analysis, which in turn can provide us with objective results and answers, within the prediction or estimation framework, about the archaeological findings. Exploratory statistical analysis was almost exclusively used initially for analyzing such data mainly because of their simplicity. The simplicity originates from the fact that exploratory techniques do not rely on any distribution assumption and conduct a non-parametric statistical analysis. However the recent development of the statistical methodology and the computing software allows us to make use of more sophisticated statistical techniques and obtain more informative results. We explore and present applications of three such techniques. The finite mixture approach for model based clustering, the latent class model and the Bayesian mixture of normal distributions with unknown number of components. All three methods can be used for identifying sub-groups in the sample and classify the items.

Research on Modeling of Brain Function Network in Major Depressive Disorder

2014 ◽  
Vol 989-994 ◽  
pp. 2037-2042
Author(s):  
Li Min Niu ◽  
Hao Guo ◽  
Jun Jie Chen
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Brain Function ◽  
Evaluation Criteria ◽  
Real Data ◽  
Class Model ◽  
Major Depressive ◽  
Common Neighbor ◽  
Model Based ◽  
Function Network

In order to analyze the gap of function network between Major depressive disorder and health person, this paper studies with modeling approach. This paper analyzes the function network of Major depressive disorder with the model based on anatomical distance and the number of common neighbor. The result shows that the distribution of the optimal brain function network is linear in all volunteer. And the slope of the linear relationship in the patients is less than health, so we hope this point can be as secondary evidence to determine the person whether fall ill. And we also propose two models and those models of brain function are based on anatomical distance or the number of common neighbor. Create the evaluation criteria for select the optimal brain function model network in each class model based on select the maximum value in the proportion of the common edges of two network accounted all edges. Select the model that can simulate the real brain function network by comparison with real data fMRI network. Finally, the results show the best model only is based on anatomical distance .

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