Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

2009 ◽  
Vol 49 (1) ◽  
pp. 48 ◽  
Author(s):  
B. J. Horton ◽  
D. L. Evans ◽  
P. J. James ◽  
N. J. Campbell
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Model Based ◽  
Possible Cause ◽  
Overall Risk

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.

scholarly journals Information Security Risk Assessment of Smartphones Using Bayesian Networks

2015 ◽  
Author(s):  
Kristian Herland ◽  
Heikki Hämmäinen ◽  
Pekka Kekolahti
Keyword(s):  
Risk Assessment ◽  
Information Security ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Security Risk ◽  
Bayesian Network Model ◽  
Information Security Risk ◽  
Security Risk Assessment ◽  
Causal Bayesian Network

This study comprises an information security risk assessment of smartphone use in Finland using Bayesian networks. The primary research method is a knowledge-based approach to build a causal Bayesian network model of information security risks and consequences. The risks, consequences, probabilities and impacts are identified from domain experts in a 2-stage interview process with 8 experts as well as from existing research and statistics. This information is then used to construct a Bayesian network model which lends itself to different use cases such as sensitivity and scenario analysis. The identified risks’probabilities follow a long tail wherein the most probable risks include unintentional data disclosure, failures of device or network, shoulder surfing or eavesdropping and loss or theft of device. Experts believe that almost 50% of users share more information to other parties through their smartphones than they acknowledge or would be willing to share. This study contains several implications for consumers as well as indicates a clear need for increasing security awareness among smartphone users.  

scholarly journals A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 240-258
Author(s):  
Nima Khakzad
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Synergistic Effects ◽  
Dynamic Bayesian Network ◽  
Event Tree ◽  
Domino Effect ◽  
Reasoning Under Uncertainty ◽  
Event Tree Analysis ◽  
Probabilistic Network

High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian networks—a type of probabilistic network for reasoning under uncertainty—have proven to be a reliable and robust technique for the modeling and risk assessment of domino effects. In the present study, applications of Bayesian networks to modeling and safety assessment of domino effects in petroleum tank terminals has been demonstrated via some examples. The tutorial starts by illustrating the inefficacy of event tree analysis in domino effect modeling and then discusses the capabilities of Bayesian network and its derivatives such as dynamic Bayesian network and influence diagram. It is also discussed how noisy OR can be used to significantly reduce the complexity and number of conditional probabilities required for model establishment.

scholarly journals Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Ane Elida Fonneløp ◽  
Sara Faria ◽  
Gnanagowry Shanthan ◽  
Peter Gill
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Crime Scene ◽  
Continuous Model ◽  
Dna Transfer ◽  
Probabilistic Framework ◽  
Model Based ◽  
Plastic Bags

When DNA from a suspect is detected in a sample collected at a crime scene, there can be alternative explanations about the activity that may have led to the transfer, persistence and recovery of his/her DNA. Previous studies have shown that DNA can be indirectly transferred via intermediate surfaces and that DNA on a previously used object can persist after subsequent use of another individual. In addition, it has been shown that a person’s shedder status may influence transfer, persistence, prevalence, and recovery of DNA. In this study we have investigated transfer persistence and recovery on zip-lock bags and tape, which are commonly encountered in drug cases and how the shedder status of the participants influenced the results. A probabilistic framework was developed which was based on a previously described Bayesian network with case-specific modifications. Continuous modelling of data was used to inform the Bayesian networks and two case scenarios were investigated. In the specific scenarios only moderate to low support for Hp was obtained. Applying a continuous model based on the profile quality can change the LRs.

Application of Bayesian Networks in Multi-Hazard Safety Assessment of Nuclear Power Plants

2020 ◽  
Author(s):  
Varenya Kumar D. Mohan ◽  
Philip Vardon ◽  
James Daniell ◽  
Pierre Gehl ◽  
Andreas Schafer ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Probability Distributions ◽  
Sensitivity Analyses ◽  
Continuous Variables ◽  
Safety Assessments

<p>Low probability events occurring in sequence, within a limited operational time (damage and recovery window between events), are a key consideration in multi-hazard safety assessments of nuclear power plants (NPPs). Cascading effects from hazards and associated event sequences could potentially have a significant impact on risk estimates. The Bayesian network can act as a framework to consider aforementioned statistical dependencies between various hazards in multi-risk analyses of nuclear power plants.</p><p>Within the EU project NARSIS (New Approach to Reactor Safety Improvements), a Bayesian network-based risk assessment framework was developed to perform multi-hazard risk assessment of NPPs.</p><p>The Bayesian network method was applied for an external-event related station blackout (SBO) scenario at a NPP. Earthquake, flooding, and tornado were among the hazards considered at a decommissioned NPP site location in Europe. Both hazard dependency in time as well as a cascading scenario was also considered. The hazards, their interactions and the fragilities of selected systems, structures and components within the nuclear power plant were represented in the network and their probability distributions were obtained based on the multi-hazard and fragility approaches adopted within the NARSIS project.</p><p>Sensitivity analyses in the network were used to identify key hazards and interactions. Most influential hazard combinations and ranges of intensity measures were identified through diagnostic inference in the network. Discretisation of continuous variables (hazard curves in this case) is a key aspect of performing inference in Bayesian networks. The effect of various levels of discretisation of hazard probability distributions was assessed, to identify suitable discretisations of hazard data.</p><p>This application demonstrates the use and advantages of the Bayesian network methodology, developed in the NARSIS project, for probabilistic safety assessments of NPPs.</p>

BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES

2007 ◽  
Vol 16 (03) ◽  
pp. 417-433 ◽  
Author(s):  
DELCROIX VÉRONIQUE ◽  
MAALEJ MOHAMED-AMINE ◽  
PIECHOWIAK SYLVAIN
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Medical Diagnosis ◽  
Probabilistic Models ◽  
Research Projects ◽  
Technical Problems ◽  
Network Applications ◽  
Model Based ◽  
Multiple Diagnosis

Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.

scholarly journals Hybrid Risk Assessment Model Based on Bayesian Networks

2016 ◽  
pp. 21-40
Author(s):  
François-Xavier Aguessy ◽  
Olivier Bettan ◽  
Gregory Blanc ◽  
Vania Conan ◽  
Hervé Debar
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Model Based
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