Risk Assessment and Decision Analysis with Bayesian Networks

Fenton, Norman

doi:10.1201/b13102

Cited by 344 publications

(392 citation statements)

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“…Under each correlation coefficient of Table 4 we present the upper and lower limits for a CI = 90%. In addition, we calculated the p-values (p-value is the probability of obtaining a result as extreme as, or more extreme than, the result actually obtained when the null hypothesis is true) for each correlation coefficient (Fenton & Neil 2012) and all p-values are included in Table 4. The most significant correlation coefficients are emphasized with bold fonts.…”

Section: Location Of the Sep Event Related Solar Flaresmentioning

confidence: 99%

Solar flares, coronal mass ejections and solar energetic particle event characteristics

Papaioannou

Sandberg

Anastasiadis

et al. 2016

J. Space Weather Space Clim.

140

129

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A new catalogue of 314 solar energetic particle (SEP) events extending over a large time span from 1984 to 2013 has been compiled. The properties as well as the associations of these SEP events with their parent solar sources have been thoroughly examined. The properties of the events include the proton peak integral flux and the fluence for energies above 10, 30, 60 and 100 MeV. The associated solar events were parametrized by solar flare (SF) and coronal mass ejection (CME) characteristics, as well as related radio emissions. In particular, for SFs: the soft X-ray (SXR) peak flux, the SXR fluence, the heliographic location, the rise time and the duration were exploited; for CMEs the plane-of-sky velocity as well as the angular width were utilized. For radio emissions, type III, II and IV radio bursts were identified. Furthermore, we utilized element abundances of Fe and O. We found evidence that most of the SEP events in our catalogue do not conform to a simple two-class paradigm, with the 73% of them exhibiting both type III and type II radio bursts, and that a continuum of event properties is present. Although, the so-called hybrid or mixed events are found to be present in our catalogue, it was not possible to attribute each SEP event to a mixed/hybrid sub-category. Moreover, it appears that the start of the type III burst most often precedes the maximum of the SF and thus falls within the impulsive phase of the associated SF. At the same time, type III bursts take place within %5.22 min, on average, in advance from the time of maximum of the derivative of the SXR flux (Neupert effect). We further performed a statistical analysis and a mapping of the logarithm of the proton peak flux at E > 10 MeV, on different pairs of the parent solar source characteristics. This revealed correlations in 3-D space and demonstrated that the gradual SEP events that stem from the central part of the visible solar disk constitute a significant radiation risk. The velocity of the associated CMEs, as well as the SXR peak flux and fluence, are all fairly significantly correlated to both the proton peak flux and the fluence of the SEP events in our catalogue. The strongest correlation to SEP characteristics is manifested by the CME velocity.

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Section: Location Of the Sep Event Related Solar Flaresmentioning

confidence: 99%

Solar flares, coronal mass ejections and solar energetic particle event characteristics

Papaioannou

Sandberg

Anastasiadis

et al. 2016

J. Space Weather Space Clim.

140

129

View full text Add to dashboard Cite

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“…The key feature of BBNs is that they enable us to model and reason about uncertainty. The BBN forces the assessor to expose all assumptions about the impact of different forms of evidence and hence provides a visible and auditable dependability or safety argument [11]. Therefore we can say that Bayesian model can be pretty much everywhere.…”

Section: Related Workmentioning

confidence: 99%

Stroke Prediction Context-Aware Health Care System

Mcheick

Nasser

Dbouk

et al. 2016

2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

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Abstract-This paper proposes a prediction framework based on ontology and Bayesian Belief Networks BBN to support a medical teams in every daily. We propose a Stroke Prediction System (SPS), a new software component to handle the uncertainty of having a stroke disease by determining the risk score level. This is composed of four layers: acquisition of data, aggregation, reasoning and application. SPS senses, collects, and analyzes data of a patient, then uses wearable sensors and the mobile application to interact with the patient and staffs. When the risk reaches critical limits, SPS notifies all concerned parties; the patient, the doctor, and the emergency department. The patient profile is also updated to reflect this urgent intervention requirement. A Bayesian model is designed and implemented using the Netica tool to prove its efficiency i) by handling patient context remotely and verifying its changes locally and ii) on predicting missing probabilities and calculate the probability of high risk level for emergency cases. The SPS system improves the accuracy of decision making and uses a new ontology of stroke disease inspired from our Parkinson ontology already developed.

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“…The scoring system underestimates the risk factors if treatment is not recorded, no matter how many other data are collected 6 . In 2013, we conducted a survey 7 of 110 stakeholders in African agriculture (including scientists, universities, donors, government ministries, NGOs, the private sector and farmer associations).…”

Section: Model Intervention Decisionsmentioning

confidence: 99%

“…We advocate a different approach. Governments and the development community need to embrace decisionanalysis concepts and tools that have been used for decades in mining, oil, cybersecurity, insurance, environmental policy and drug development 5,6 . Our call to adopt this approach is based on five principles.…”

mentioning

confidence: 99%