Approximate structure learning for large Bayesian networks

Scanagatta, Mauro; Corani, Giorgio; Campos, Cassio P. de; Zaffalon, Marco

doi:10.1007/s10994-018-5701-9

Cited by 37 publications

(37 citation statements)

References 14 publications

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“…Identification of the parent set and an optimized structure allow easy learning in Bayesian networks. A dynamic Bayesian network deals with uncertainties following the addition of time-related information [39]. The probabilistic prediction model requires learning of the structure and parameters, as well as probabilistic reasoning.…”

Section: Related Workmentioning

confidence: 99%

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Latha

Pandu

2020

Sensors

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This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

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Section: Related Workmentioning

confidence: 99%

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Latha

Pandu

2020

Sensors

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“…This fact has been reflected in the scope and generalization of discussion on the results and conclusions. This is mainly due to the characteristics of the Bayesian analysis that, even though the missing variables are suitably supported, are affected by the low number of variables included in the study or, conversely, by their high number ( Scanagatta et al, 2018 ).…”

Section: Limitations Of This Studymentioning

confidence: 99%

Studying Well and Performing Well: A Bayesian Analysis on Team and Individual Rowing Performance in Dual Career Athletes

et al. 2020

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On many occasions, the maximum result of a team does not equate to the total maximum individual effort of each athlete (social loafing). Athletes often combine their sports life with an academic one (Dual Career), prioritizing one over the over in a difficult balancing act. The aim of this research is to examine the existence of social loafing in a group of novice university rowers and the differences that exist according to sex, academic performance, and the kind of sport previously practiced (individual or team). Therefore, a study was conducted from a probabilistic perspective using the Bayesian Network analysis methodology. The results confirm the existence of the Ringelmann effect or social loafing. The Bayesian analysis let us confirm that having a good student who practices a team sport, even in the individual rowing concept, increases the probability of obtaining greater performance (higher number of strokes and more power in each one). Therefore, when rowing partnerships are formed, the occurrence probability chain is quickly simplified, along with values of the top and bottom variables. Finally, the instantiations undertaken on the bottom variable that appears to be common in the two BNs, the watt input, enhance the results obtained. In short, rowers who have a better academic record are more involved in team testing, so this characteristic is defining when it comes to achieving better performance in team testing.

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“…However, this method cannot share subexpressions in its decomposable pattern expressions. In addition, Scanagatta et al 38 presented approximate structure learning algorithms for Bayesian networks, where they improved on state-of-the-art methods that rely on an ordering-based search by sampling more effectively the space of the orders, including parent set identification, structure optimization, and structure optimization under bounded treewidth. Liu and Liu 39 proposed a maximum relevance minimum common redundancy (mRMCR) algorithm based on the information theory and feature selection, where they established a mutual information solution formula on the preference database and designed a formula for calculating mRMCR.…”

Section: Related Workmentioning

confidence: 99%

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Wang

Lin³

et al. 2020

International Journal of Distributed Sensor Networks

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Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

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Approximate structure learning for large Bayesian networks

Cited by 37 publications

References 14 publications

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Studying Well and Performing Well: A Bayesian Analysis on Team and Individual Rowing Performance in Dual Career Athletes

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

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