A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

Jin, Xu; Zhao, Yue; Chen, Jian; Han, Chao

doi:10.1007/s11390-015-1556-8

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Learning the optional structure of a Bayesian network is NP-hard problem, which means that it requires a huge amount of computation and may not converge to global optima. Prior knowledge of the variables can be incorporated by their dependencies, such as the existence or absence of an edge and even the probability distribution ( Su et al., 2014 ; Xu et al., 2015 ). The results of learned networks get improved while computational cost is reduced.…”

Section: Methods To Integrate Human Knowledgementioning

confidence: 99%

Integrating Machine Learning with Human Knowledge

et al. 2020

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Summary Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.

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Section: Methods To Integrate Human Knowledgementioning

confidence: 99%

Integrating Machine Learning with Human Knowledge

et al. 2020

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“…In such instances, a purely data driven approach to learning the network would be time-consuming due to the large parameter space, and inefficiency at identifying an approximation of the true network structure. Prior knowledge about dependencies between variables can therefore be included in the network structure, while still allowing a data driven approach for unknown dependencies, to improve the overall computation of the network structure (Heckerman et al, 1995 ; Xu et al, 2015 ). The following sections detail the steps taken in the current study to firstly prepare the data for each network, and then obtain the structure of each network that was used for further inference.…”

Section: Methodsmentioning

confidence: 99%

An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes

Fisher¹,

Gittoes²,

Evans³

et al. 2020

Front. Sports Act. Living

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This paper adopts a novel, interdisciplinary approach to explore the relationship between stress-related psychosocial factors, physiological markers and occurrence of injury in athletes using a repeated measures prospective design. At four data collection time-points, across 1-year of a total 2-year data collection period, athletes completed measures of major life events, the reinforcement sensitivity theory personality questionnaire, muscle stiffness, heart rate variability and postural stability, and reported any injuries they had sustained since the last data collection. Two Bayesian networks were used to examine the relationships between variables and model the changes between data collection points in the study. Findings revealed muscle stiffness to have the strongest relationship with injury occurrence, with high levels of stiffness increasing the probability of sustaining an injury. Negative life events did not increase the probability of injury occurrence at any single time-point; however, when examining changes between time points, increases in negative life events did increase the probability of injury. In addition, the combination of increases in negative life events and muscle stiffness resulted in the greatest probability of sustaining an injury. Findings demonstrated the importance of both an interdisciplinary approach and a repeated measures design to furthering our understanding of the relationship between stress-related markers and injury occurrence.

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“…Additionally, the performance of the algorithmic learning methods can be improved by including prior knowledge about a part of the structure while learning (Mansinghka et al, 2012;Xu et al, 2015). This ensures that the algorithm learns the structure more efficiently by capturing the prior distribution present in the data that would otherwise be overlooked.…”

Section: Learning Structural Causal Modelsmentioning

confidence: 99%

Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments

Rentala

Zhu

Mukhopadhyay

2021

Front. Sustain. Cities

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Identification and quantitative understanding of factors that influence occupant energy behavior and thermal state during the design phase are critical in supporting effective energy-efficient design. To achieve this, immersive virtual environments (IVEs) have recently shown potential as a tool to simulate occupant energy behaviors and collect context-dependent behavior data for buildings under design. On the other hand, prior models of occupant energy behaviors and thermal states used correlation-based approaches, which failed to capture the underlying causal interactions between the influencing factors and hence were unable to uncover the true causing factors. Therefore, in this study, the authors investigate the applicability of causal inference for identifying the causing factors of occupant/participant energy behavioral intentions and their thermal states in IVE condition and compare those results with the baseline in-situ condition. The energy behavioral intentions here are a proximal antecedent of actual energy behaviors. A set of experiments involving 72 human subjects were performed through the use of a head-mounted device (HMD) in a climate chamber. The subjects were exposed to three different step temperatures (cool, neutral, warm) under an IVE and a baseline in-situ condition. Participants' individual factors, behavioral factors, skin temperatures, virtual experience factors, thermal states (sensation, acceptability, comfort), and energy behavioral intentions were collected during the experiments. Structural causal models were learnt from data using the elicitation method in conjunction with the PC-Stable algorithm. The findings show that the causal inference framework is a potentially effective method for identifying causing factors of thermal states and energy behavioral intentions as well as quantifying their causal effects. In addition, the study shows that in IVE experiments, the participants' virtual experience factors such as their immersion, presence, and cybersickness were not the causing factors of thermal states and energy behavioral intentions. Furthermore, the study suggests that participants' behavioral factors such as their attitudes toward energy conservation and perceived behavioral control to conserve energy were the causing factors of their energy behavioral intentions. Also, the indoor temperature was a causing factor of general thermal sensation and overall skin temperature. The paper also discusses other findings, including discrepancies, limitations of the study, and recommendations for future studies.

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A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

Cited by 12 publications

References 12 publications

Integrating Machine Learning with Human Knowledge

Integrating Machine Learning with Human Knowledge

An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes

Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments

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