The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling u...The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.展开更多
In recent years,the development of machine learning has introduced new analytical methods to theoretical research,one of which is Bayesian network—a probabilistic graphical model well-suited for modelling complex non...In recent years,the development of machine learning has introduced new analytical methods to theoretical research,one of which is Bayesian network—a probabilistic graphical model well-suited for modelling complex non-deterministic systems.A recent study has revealed that the order in which variables are read from data can impact the structure of a Bayesian network(Kitson and Constantinou in The impact of variable ordering on Bayesian Network Structure Learning,2022.arXiv preprint arXiv:2206.08952).However,in empirical studies,the variable order in a dataset is often arbitrary,leading to unreliable results.To address this issue,this study proposed a hybrid method that combined theory-driven and data-driven approaches to mitigate the impact of variable ordering on the learning of Bayesian network structures.The proposed method was illustrated using an empirical study predicting depression and aggressive behavior in high school students.The results demonstrated that the obtained Bayesian network structure is robust to variable orders and theoretically interpretable.The commonalities and specificities in the network structure of depression and aggressive behavior are both in line with theorical expectations,providing empirical evidence for the validity of the hybrid method.展开更多
Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony opt...Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.展开更多
Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms fo...Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.展开更多
This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock...This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.展开更多
Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Prev...Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.展开更多
Background:Beijing sub-pedigree 2(BSP2)and T sub-lineage 6(TSL6)are two clades belonging to Beijing and T family of Mycobacterium tuberculosis(MTB),respectively,defined by Bayesian population structure analysis based ...Background:Beijing sub-pedigree 2(BSP2)and T sub-lineage 6(TSL6)are two clades belonging to Beijing and T family of Mycobacterium tuberculosis(MTB),respectively,defined by Bayesian population structure analysis based on 24-loci mycobacterial interspersed repetitive unit-variable number of tandem repeats(MIRU-VNTR).Globally,over 99%of BSP2 and 89%of TSL6 isolates were distributed in Chongqing,suggesting their possible local adaptive evolution.The objective of this paper is to explore whether BSP2 and TSL6 originated by their local adaptive evolution from the specific isolates of Beijing and T families in Chongqing.Methods:The genotyping data of 16090 MTB isolates were collected from laboratory collection,published literatures and SITVIT database before subjected to Bayesian population structure analysis based on 24-loci MIRUVNTR.Spacer Oligonucleotide Forest(Spoligoforest)and 24-loci MIRU-VNTR-based minimum spanning tree(MST)were used to explore their phylogenetic pathways,with Bayesian demographic analysis for exploring the recent demographic change of TSL6.Results:Phylogenetic analysis suggested that BSP2 and TSL6 in Chongqing may evolve from BSP4 and TSL5,respectively,which were locally predominant in Tibet and Jiangsu,respectively.Spoligoforest showed that Beijing and T families were genetically distant,while the convergence of the MIRU-VNTR pattern of BSP2 and TSL6 was revealed by WebLogo.The demographic analysis concluded that the recent demographic change of TSL6 might take 111.25 years.Conclusions:BSP2 and TSL6 clades might originate from BSP4 and TSL5,respectively,by their local adaptive evolution in Chongqing.Our study suggests MIRU-VNTR be combined with other robust markers for a more comprehensive genotyping approach,especially for families of clades with the same MIRU-VNTR pattern.展开更多
Objective:To estimate the potential causal impact of Enterovirus A71(EV71)vaccination program on the reduction of EV71-infected hand,foot,and mouth disease(HFMD)in Zhejiang Province.Methods:We utilized the longitudina...Objective:To estimate the potential causal impact of Enterovirus A71(EV71)vaccination program on the reduction of EV71-infected hand,foot,and mouth disease(HFMD)in Zhejiang Province.Methods:We utilized the longitudinal surveillance dataset of HFMD and EV71 vaccination in Zhejiang Province during 2010-2019.We estimated vaccine efficacy using a Bayesian structured time series(BSTS)model,and employed a negative control outcome(NCO)model to detect unmeasured confounding and reveal potential causal association.Results:We estimated that 20,132 EV71 cases(95%CI:16,733,23,532)were prevented by vaccination program during 2017-2019,corresponding to a reduction of 29%(95%CI:24%,34%).The effectiveness of vaccination increased annually,with reductions of 11%(95%CI:6%,16%)in 2017 and 66%(95%CI:61%,71%)in 2019.Children under 5 years old obtained greater benefits compared to those over 5 years.Cities with higher vaccination coverage experienced a sharper EV71 reduction compared to those with lower coverage.The NCO model detected no confounding factors in the association between vaccination and EV71 cases reduction.展开更多
Gold has multiple attributes and its price is affected by various factors in the market.This paper studies the dynamic relationship between the gold price returns and its affecting factors.Then we use the STL-ETS,neur...Gold has multiple attributes and its price is affected by various factors in the market.This paper studies the dynamic relationship between the gold price returns and its affecting factors.Then we use the STL-ETS,neural network and Bayesian structural time series model to predict the gold price returns,and compare their performance with the benchmark models.The results show that the shocks of crude oil returns and VIX have the positive effect on gold price returns,the shocks of the US dollar index have the negative effect on gold price returns.And the fluctuation of gold price returns mainly depends on crude oil price returns shocks.STL-ETS model can accurately fit the fluctuation trend of the gold price returns and improve prediction accuracy.展开更多
This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Inter...This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Internet search and media indices,social media discussions,and product purchases.Using a Bayesian structural equation modeling approach,we show that online public attention to air pollution increases when air pollution increases,and such attention is captured by more media reports,social media discussions,and Internet searches.A comprehensive relationship involving direct and indirect effects between air pollution and precautionary behavior is established.Air pollution has a positive effect on proactive defensive behaviors,reflected in increased purchases of preventive products,and this effect is partially mediated by online media coverage and the public's Internet searches.Air pollution also motivates passive defensive behaviors,reflected in decreased purchases of outdoor sports products,and this effect is partially mediated by social media coverage.These results suggest that governments could improve the quality of policy making by considering the different roles of various forms of online public attention in the public's risk perceptions of and reactions to air pollution.展开更多
文摘The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.
基金supported by National Natural Science Foundation of China(Grant No.32171089)Research Fund from Hangzhou Mingshitang Education Technology Development Co.,Ltd.(Project No.1222000035).
文摘In recent years,the development of machine learning has introduced new analytical methods to theoretical research,one of which is Bayesian network—a probabilistic graphical model well-suited for modelling complex non-deterministic systems.A recent study has revealed that the order in which variables are read from data can impact the structure of a Bayesian network(Kitson and Constantinou in The impact of variable ordering on Bayesian Network Structure Learning,2022.arXiv preprint arXiv:2206.08952).However,in empirical studies,the variable order in a dataset is often arbitrary,leading to unreliable results.To address this issue,this study proposed a hybrid method that combined theory-driven and data-driven approaches to mitigate the impact of variable ordering on the learning of Bayesian network structures.The proposed method was illustrated using an empirical study predicting depression and aggressive behavior in high school students.The results demonstrated that the obtained Bayesian network structure is robust to variable orders and theoretically interpretable.The commonalities and specificities in the network structure of depression and aggressive behavior are both in line with theorical expectations,providing empirical evidence for the validity of the hybrid method.
基金supported by the National Natural Science Foundation of China (60974082,11171094)the Fundamental Research Funds for the Central Universities (K50510700004)+1 种基金the Foundation and Advanced Technology Research Program of Henan Province (102300410264)the Basic Research Program of the Education Department of Henan Province (2010A110010)
文摘Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.
基金Supported by the National Natural Science Foundation of China(61403290,11301408,11401454)the Foundation for Youths of Shaanxi Province(2014JQ1020)+1 种基金the Foundation of Baoji City(2013R7-3)the Foundation of Baoji University of Arts and Sciences(ZK15081)
文摘Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.
文摘This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.
基金supported by the MOE(Ministry of Education)Project of Humanities and Social Science of China[23YJA190007]the Natural Science Foundation of Guangdong Province[2022A1515010367]the Key Research and Development Plan of Yunnan Province,China[202203AC100003].
文摘Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.
基金This work was financially supported by the Department of Science and Technology of Sichuan(18GJHZ0137).
文摘Background:Beijing sub-pedigree 2(BSP2)and T sub-lineage 6(TSL6)are two clades belonging to Beijing and T family of Mycobacterium tuberculosis(MTB),respectively,defined by Bayesian population structure analysis based on 24-loci mycobacterial interspersed repetitive unit-variable number of tandem repeats(MIRU-VNTR).Globally,over 99%of BSP2 and 89%of TSL6 isolates were distributed in Chongqing,suggesting their possible local adaptive evolution.The objective of this paper is to explore whether BSP2 and TSL6 originated by their local adaptive evolution from the specific isolates of Beijing and T families in Chongqing.Methods:The genotyping data of 16090 MTB isolates were collected from laboratory collection,published literatures and SITVIT database before subjected to Bayesian population structure analysis based on 24-loci MIRUVNTR.Spacer Oligonucleotide Forest(Spoligoforest)and 24-loci MIRU-VNTR-based minimum spanning tree(MST)were used to explore their phylogenetic pathways,with Bayesian demographic analysis for exploring the recent demographic change of TSL6.Results:Phylogenetic analysis suggested that BSP2 and TSL6 in Chongqing may evolve from BSP4 and TSL5,respectively,which were locally predominant in Tibet and Jiangsu,respectively.Spoligoforest showed that Beijing and T families were genetically distant,while the convergence of the MIRU-VNTR pattern of BSP2 and TSL6 was revealed by WebLogo.The demographic analysis concluded that the recent demographic change of TSL6 might take 111.25 years.Conclusions:BSP2 and TSL6 clades might originate from BSP4 and TSL5,respectively,by their local adaptive evolution in Chongqing.Our study suggests MIRU-VNTR be combined with other robust markers for a more comprehensive genotyping approach,especially for families of clades with the same MIRU-VNTR pattern.
基金supported the grants from National Key R&D Program of China (2022YFC2305305)by grants from consultancy project (2022-JB-06)by the Chinese Academy of Engineering (CAE)the Bill&Melinda Gates Foundation[Grant Number:INV-016826].
文摘Objective:To estimate the potential causal impact of Enterovirus A71(EV71)vaccination program on the reduction of EV71-infected hand,foot,and mouth disease(HFMD)in Zhejiang Province.Methods:We utilized the longitudinal surveillance dataset of HFMD and EV71 vaccination in Zhejiang Province during 2010-2019.We estimated vaccine efficacy using a Bayesian structured time series(BSTS)model,and employed a negative control outcome(NCO)model to detect unmeasured confounding and reveal potential causal association.Results:We estimated that 20,132 EV71 cases(95%CI:16,733,23,532)were prevented by vaccination program during 2017-2019,corresponding to a reduction of 29%(95%CI:24%,34%).The effectiveness of vaccination increased annually,with reductions of 11%(95%CI:6%,16%)in 2017 and 66%(95%CI:61%,71%)in 2019.Children under 5 years old obtained greater benefits compared to those over 5 years.Cities with higher vaccination coverage experienced a sharper EV71 reduction compared to those with lower coverage.The NCO model detected no confounding factors in the association between vaccination and EV71 cases reduction.
基金supported by the National Natural Science Foundation of China(NSFC)(71874133)the Annual Basic Scientific Research Project of Xidian University(2019)
文摘Gold has multiple attributes and its price is affected by various factors in the market.This paper studies the dynamic relationship between the gold price returns and its affecting factors.Then we use the STL-ETS,neural network and Bayesian structural time series model to predict the gold price returns,and compare their performance with the benchmark models.The results show that the shocks of crude oil returns and VIX have the positive effect on gold price returns,the shocks of the US dollar index have the negative effect on gold price returns.And the fluctuation of gold price returns mainly depends on crude oil price returns shocks.STL-ETS model can accurately fit the fluctuation trend of the gold price returns and improve prediction accuracy.
基金Dr.Xu and Dr.Feng contributed equally to this work.Dr.Xu's work was partially supported by the National Natural Science Foundation of China(71704052 and 72074072)the Natural Science Foundation of Hunan Province,China(2018JJ3263)+5 种基金the Research Foundation of Education Bureau of Hunan Province,China(18B334)Dr.Feng's work was partially supported by the National Natural Science Foundation of China(71802166)the Humanities and Social Science Foundation of the Ministry of Education of China(20YJC630055)Dr.Li's work was partially supported by the LamWoo Research Fund(LWI20005)Faculty Research Grant(DB20A3 and DB21A7)Direct Grant(DR21B3).
文摘This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Internet search and media indices,social media discussions,and product purchases.Using a Bayesian structural equation modeling approach,we show that online public attention to air pollution increases when air pollution increases,and such attention is captured by more media reports,social media discussions,and Internet searches.A comprehensive relationship involving direct and indirect effects between air pollution and precautionary behavior is established.Air pollution has a positive effect on proactive defensive behaviors,reflected in increased purchases of preventive products,and this effect is partially mediated by online media coverage and the public's Internet searches.Air pollution also motivates passive defensive behaviors,reflected in decreased purchases of outdoor sports products,and this effect is partially mediated by social media coverage.These results suggest that governments could improve the quality of policy making by considering the different roles of various forms of online public attention in the public's risk perceptions of and reactions to air pollution.
