Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network

2003 ◽  
Vol 1836 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Francisco J. Torres ◽  
Manfred Huber
Keyword(s):  
Graphical Model ◽  
Causal Model ◽  
Household Survey ◽  
Bayesian Belief Network ◽  
Generation Process ◽  
Data Set ◽  
Belief Network ◽  
Representation Structure ◽  
Key Variables ◽  
Household Survey Data

A Bayesian belief network (BBN) is a modeling and knowledge-representation structure used in artificial intelligence that consists of a graphical model depicting probabilistic relationships among variables of interest. This graphical model is a valuable tool for representing the causal relationships in a given set of variables. Because the number of possible BBNs for a given data set is exponential with respect to the number of variables, learning a BBN from data is a difficult and resource-consuming task. A greedy algorithm that automatically constructs a BBN from a data set of cases obtained from a household survey was implemented. The resulting BBN shows the dependencies among key variables that are associated with the trip-generation process.

scholarly journals Reducing the Complexity of Casual Representation in Bayesian Belief Network

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1243
Author(s):  
Yit Yin Wee ◽  
Shing Chiang Tan ◽  
KuokKwee Wee
Keyword(s):  
Causal Model ◽  
Knowledge Engineering ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Joint Probability Distribution ◽  
Causal Knowledge ◽  
Belief Network ◽  
Track Record ◽  
Probability Table ◽  
Causal Framework

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool.   Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model.  Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN.   Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

scholarly journals Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

2021 ◽  
Vol 18 (6) ◽  
pp. 9233-9252
Author(s):  
Mahmood Ahmad ◽  
◽  
Feezan Ahmad ◽  
Jiandong Huang ◽  
Muhammad Junaid Iqbal ◽  
...  
Keyword(s):  
Graphical Model ◽  
Influence Factors ◽  
Structural Modeling ◽  
Bayesian Belief Network ◽  
Soil Liquefaction ◽  
Support Vector ◽  
Probabilistic Graphical Model ◽  
Interpretive Structural Modeling ◽  
Liquefaction Potential ◽  
Belief Network

<abstract> <p>This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.</p> </abstract>

scholarly journals Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

2021 ◽  
Author(s):  
Mahmood Ahmad ◽  
Xiao-Wei Tang ◽  
Feezan Ahmad ◽  
Nima Pirhadi ◽  
Xusheng Wan ◽  
...  
Keyword(s):  
Graphical Model ◽  
Influence Factors ◽  
Structural Modeling ◽  
Bayesian Belief Network ◽  
Soil Liquefaction ◽  
Support Vector ◽  
Probabilistic Graphical Model ◽  
Interpretive Structural Modeling ◽  
Liquefaction Potential ◽  
Belief Network

Abstract This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict CPT-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naïve Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

The correlations between livelihood capitals and perceived recovery

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziqiang Han ◽  
Lei Wang ◽  
Jianwen Wei
Keyword(s):  
Household Survey ◽  
Physical Capital ◽  
Current Status ◽  
Sustainable Livelihood ◽  
Data Set ◽  
Content Type ◽  
2008 Wenchuan Earthquake ◽  
Household Survey Data ◽  
Sustainable Livelihood Approach ◽  
Livelihood Capitals

PurposeThis study examines the recovery of households after disasters from the sustainable livelihood approach (SLA) perspective.Design/methodology/approachThis study analyzes the perception of recovery by using a longitudinal household survey data set collected from a Chinese county devastated by the 2008 Wenchuan earthquake. The analysis compares the changes of livelihood capitals (financial, natural, physical, social, human) between 2012 and 2009 and recovery perception.FindingsThe results demonstrate that both the current status of financial, natural, and social capital and the changes of the capitals between 2009 and 2012 are positively correlated with the perceived level of recovery. The associations between the current status and the change of physical capital and recovery perception are insignificant. In contrast, with a greater change of human capital between 2009 and 2012, participants have a lower perception of recovery.Originality/valueBy investigating a longitudinal data, this study indicates that (1) household recovery should be considered as multidimensional, (2) the SLA could be a feasible framework to measure recovery, and (3) individual's recovery perception is dependent on the various dimensions of recovery measures.

Immiserizing Growth

2019 ◽  
Keyword(s):  
Political Economy ◽  
Secondary Data ◽  
Household Survey ◽  
Data Set ◽  
Republic Of China ◽  
The Poor ◽  
Poverty Measures ◽  
Entry Points ◽  
Country Analysis ◽  
Cross Country

Immiserizing Growth occurs when growth fails to benefit, or harms, those at the bottom. It is not a new concept, appearing such figures as Malthus, Ricardo and Marx. It is also not empirically insignificant, occurring in between 10% and 35% of cases, depending on the data set and the growth and poverty measures used. In spite of this, it has not received its due attention in the academic literature, dominated by the prevailing narrative that ‘growth is good for the poor’. The chapters in this volume aim to arrive at a better understanding of when, why and how growth fails the poor. They combine discussion of mechanisms of Immiserizing Growth with empirical data on trends in growth, poverty and related welfare indicators. In terms of mechanisms, politics and political economy are chosen as useful entry points to explain IG episodes. The disciplinary focus is diverse, drawing on economics, political economy, applied social anthropology, and development studies. A number of methodological approaches are represented including statistical analysis of household survey and cross-country data, detailed ethnographic work and case study analysis drawing on secondary data. Geographical coverage is wide including Bolivia, the Dominican Republic, Ecuador, India, Indonesia, Mexico, Nigeria, the People’s Republic of China, Singapore, and South Korea, in addition to cross-country analysis. As the first book-length treatment of Immiserizing Growth in the literature, we believe that this volume constitutes an important step in redirecting attention to this issue.

Queens of the Desert: Convictism and Marital Attitudes across Australia

2020 ◽  
Vol 110 ◽  
pp. 457-462
Author(s):  
Victoria Baranov ◽  
Ralph De Haas ◽  
Pauline Grosjean
Keyword(s):  
Nineteenth Century ◽  
Social Norms ◽  
Spatial Variation ◽  
Survey Data ◽  
Household Survey ◽  
Same Sex ◽  
Same Sex Marriage ◽  
Founder Populations ◽  
Household Survey Data ◽  
Combined Data

We merge data on spatial variation in the presence of convicts across eighteenth and nineteenth century Australia with results from the country's 2017 poll on same-sex marriage and with household survey data. These combined data allow us to identify the lasting impact of convict colonization on social norms about marriage. We find that in areas with higher historical convict concentrations, more Australians recently voted in favor of same-sex marriage and hold liberal views about marriage more generally. Our results highlight how founder populations can have lasting effects on locally held social norms.

scholarly journals Increasing Local Fish Consumption: A Bayesian Belief Network Analysis

2021 ◽  
Vol 33 (1) ◽  
pp. 104-121
Author(s):  
Samantha Paredes ◽  
Sean Pascoe ◽  
Louisa Coglan ◽  
Carol Richards
Keyword(s):  
Network Analysis ◽  
Fish Consumption ◽  
Bayesian Belief Network ◽  
Belief Network
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