A new method to predict the interference effect in quantum-like Bayesian networks

Dai, Jiongyu; Deng, Yong

doi:10.1007/s00500-020-04693-2

Cited by 34 publications

(18 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QBNs were introduced by Tucci [ 8 ] as a means of calculating quantum mechanical conditional probabilities in the field of magnetophysics. The theory and application of QBNs is published mostly in literature pertaining to problems in physics [ 9 , 10 ], and in social psychology and cognitive science related to irrational decision-making [ 11 , 12 , 13 , 14 , 15 ]. In general, QBNs can be used to describe and solve a variety of problems in probability which are unsolvable by, or too complex for, traditional BNs, essentially where the outcomes are nonintuitive and defy traditional probability calculus ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

Marcot

2021

Entropy

View full text Add to dashboard Cite

A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be >1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo’s paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures—such as state-and-transition models, predator–prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more—could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.

show abstract

Section: Methodsmentioning

confidence: 99%

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

Marcot

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…In the framework of Bayesian learning, the main task is to calculate the posterior probability distribution p(y k+1 | s k+1 ) of the product degradation state when the realtime online measurement data s k+1 is known [42][43][44][45][46][47]. s k+1 is processed and transmitted to the Bi-LSTM network to obtain the product degradation index predicted output value At time k + 1, the new online measurement data s k+1 is processed and transmitted to the Bi-LSTM network to obtain the product degradation index prediction output z k+1 .…”

Section: Life Prediction Methods Based On Particle Filter Algorithmmentioning

confidence: 99%

A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things

Zhou

Liu

et al. 2020

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.

show abstract

“…In the article "A new method to predict the interference effect in quantum-like Bayesian networks," by Dai and Deng (2020), the authors discuss decision-making problems under the light of a quantum-like probabilistic approach. These quantum models have been developed to model decisionmaking situations which are difficult to describe using classical probability theory.…”

Section: Editors Presentationmentioning

confidence: 99%

Special issue: quantum structures and quantum information theory

et al. 2020

View full text Add to dashboard Cite

A new method to predict the interference effect in quantum-like Bayesian networks

Cited by 34 publications

References 63 publications

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things

Special issue: quantum structures and quantum information theory

Contact Info

Product

Resources

About