Qualitative-probabilistic-network-based modeling of temporal causalities and its application to feedback loop identification

Liu, Weiyi; Yue, Kun; Liu, Shuang-Xian; Sun, Yin-Bo

doi:10.1016/j.ins.2007.11.021

Cited by 10 publications

(13 citation statements)

References 29 publications

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“…Druzdzel proposed the efficient reasoning algorithm for QPNs [7,8]. We adopted QPNs as the basis of modeling temporal causalities and knowledge discovery in time-series environments [14].…”

Section: Related Workmentioning

confidence: 99%

“…In our method, an EQPN is constructed as a minimal I -map of the given sample data. Therefore, if any two variables are conditionally independent, we believe that they will not be linked directly in the DAG structure of an EQPN, which is addressed in Theorem 1 inspired by the conclusion given in [14]. Given a directed edge (A, B) in G, we suppose that sign(A, B) can be '0'.…”

Section: Reducing Unknown Qualitative Influences In Qpnsmentioning

confidence: 99%

“…Particularly, qualitative probabilistic networks (QPNs) are proposed as the qualitative abstraction of general BNs [7,29], and have been used in real applications [14]. A QPN encodes variables by a directed acyclic graph (DAG) like a Bayesian belief network, while summarizes the probabilistic relationships among variables by qualitative influences.…”

Section: Introductionmentioning

confidence: 99%

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Qualitative probabilistic networks with reduced ambiguities

Yue

Yao

et al. 2008

Appl Intell

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A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.

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“…Druzdzel proposed the efficient reasoning algorithm for QPNs [7,8]. We adopted QPNs as the basis of modeling temporal causalities and knowledge discovery in time-series environments [14].…”

Section: Related Workmentioning

confidence: 99%

Section: Reducing Unknown Qualitative Influences In Qpnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Qualitative probabilistic networks with reduced ambiguities

Yue

Yao

et al. 2008

Appl Intell

Self Cite

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“…There are many kinds of networks which can be used to represent the probabilistic knowledge, such as Bayesian Networks (BNs) [1], Dynamic Bayesian Networks (DBNs) [2,3], Qualitative Probabilistic Networks (QPNs) [4] and Temporal Qualitative Probabilistic Networks (TQPNs) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Based on these ideas, Liu and Yue [5] have proposed TQPN and implemented the qualitative and temporal knowledge representation. They construct TQPN's structure by considering the relationships between variables existing not only in each time slice, but also in adjacent time slices.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multiple Temporal Qualitative Probabilistic Networks in Time Series Environments

Lv¹,

Liao

2010

IJIES

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Abstract:The integration of uncertain information from different time sources is a crucial issue in various applications. In this paper, we propose an integration method of multiple Temporal Qualitative Probabilistic Networks (TQPNs) in time series environments. First, we present the method for learning TQPN from time series data. The TQPN's structure is constructed using Dynamic Bayesian Networks learning based on Markov Chain Monte Carlo. Furthermore, the corresponding qualitative influences are obtained by the conditional probabilities. Secondly, based on rough set theory, we integrate multiple TQPNs into a single QPN that preserves as much information as possible. Specifically, we take the rough-set-based dependency degree as the strength of qualitative influence, and then make the rules to solve the ambiguities reduction and cycles deletion problems which arise from the integration of different TQPNs. Finally, we verify the feasibility of the integration method by the simulation experiments.

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