Detection of Factors Affecting State Transition Based on Non-Homogeneous Markov Chain Model

The dependent and independent variables in traditional linear regression models are continuous numerical variables. When the dependent variable or independent variable is a discrete variable, the traditional linear regression model can no longer be used to analyze. To solve this problem, this articl...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.102490-102496
Main Authors: Xiujuan, Shen, Hefei, Liu, Yong, Li, Bin, Yuan
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Bayes methods
Bayesian estimation
Colleges & universities
Continuity (mathematics)
Dependent variables
Estimation
Hidden Markov models
Impact analysis
Independent variables
Markov analysis
Markov chains
Markov processes
Mathematical model
MCMC algorithm
Non-homogeneous Markov chain model
Numerical models
Parameter estimation
Regression analysis
Regression models
state transition
Variables
Citations: Items that this one cites
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Summary:The dependent and independent variables in traditional linear regression models are continuous numerical variables. When the dependent variable or independent variable is a discrete variable, the traditional linear regression model can no longer be used to analyze. To solve this problem, this article introduces the non-homogeneous Markov chain model. It introduces the mathematical definition of the non-homogeneous Markov chain model. And then this article uses Bayesian estimation method to derive posterior distribution of model parameters. Through the MCMC algorithm, we simulate an experiment, posterior means value of the parameters is estimated, and the estimation effect is found to be better. Finally, we analyze the impact of learning state transition about college students on the non-homogeneous Markov chain model. Influencing factors include whether to receive a scholarship and whether to serve as a class leader. In this paper, non-homogeneous Markov chain model is used to analyze and detect the impact of discrete variables on dependent variables. This is the major innovation in this article.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3097363