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

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.

Download Full-text

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

Animal Production Science ◽

10.1071/ea07179 ◽

2009 ◽

Vol 49 (1) ◽

pp. 48 ◽

Cited By ~ 8

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.

Download Full-text

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

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213007003345 ◽

2007 ◽

Vol 16 (03) ◽

pp. 417-433 ◽

Cited By ~ 4

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.

Download Full-text

XML document information retrieval model based on four-layered Bayesian network

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.02791 ◽

2009 ◽

Vol 29 (10) ◽

pp. 2791-2795

Author(s):

Xiao-long ZHANG ◽

Xing-chen HENG

Keyword(s):

Information Retrieval ◽

Bayesian Network ◽

Retrieval Model ◽

Model Based ◽

Xml Document

Download Full-text

Development of Intelligent Effort Estimation Model Based on Fuzzy Logic Using Bayesian Networks

Communications in Computer and Information Science - Software Engineering, Business Continuity, and Education ◽

10.1007/978-3-642-27207-3_9 ◽

2011 ◽

pp. 74-84 ◽

Cited By ~ 1

Author(s):

Jahangir Khan ◽

Zubair A. Shaikh ◽

Abou Bakar Nauman

Keyword(s):

Fuzzy Logic ◽

Bayesian Networks ◽

Effort Estimation ◽

Estimation Model ◽

Model Based

Download Full-text

Application of Bayesian Network in Safety Evaluation of Metro Construction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.838-841.1463 ◽

2013 ◽

Vol 838-841 ◽

pp. 1463-1468

Author(s):

Xiang Ke Liu ◽

Zhi Shen Wang ◽

Hai Liang Wang ◽

Jun Tao Wang

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Posterior Probability ◽

Fault Tree ◽

Fault Tree Analysis ◽

Safety Evaluation ◽

Tree Analysis ◽

Tunnel Blasting ◽

Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

Download Full-text

Probabilistic Model-Based Fault Diagnosis of the Rotor System

ASME 2007 Power Conference ◽

10.1115/power2007-22072 ◽

2007 ◽

Author(s):

Jiye Shao ◽

Rixin Wang ◽

Jingbo Gao ◽

Minqiang Xu

Keyword(s):

Fault Diagnosis ◽

Bayesian Network ◽

Network Model ◽

Rotating Machinery ◽

Rotor System ◽

Bayesian Network Model ◽

Diagnosis System ◽

Model Based ◽

Core Components ◽

Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Download Full-text

Target Intention Inference Model Based on Variable Structure Bayesian Network

10.1109/cise.2009.5365659 ◽

2009 ◽

Cited By ~ 1

Author(s):

Yuan Song ◽

Xin-Hua Zhang ◽

Zhi-kai Wang

Keyword(s):

Bayesian Network ◽

Variable Structure ◽

Inference Model ◽

Model Based ◽

Intention Inference

Download Full-text

Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

Scientific Programming ◽

10.1155/2016/7109235 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Hao Zhang ◽

Liyu Zhu ◽

Shensi Xu

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

System Reliability ◽

Distribution System ◽

Complex Problem ◽

Practical Significance ◽

Correlation Degree ◽

Urban Distribution ◽

Distribution System Reliability ◽

The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

Download Full-text

A dynamic Bayesian network approach for prognosis computations on discrete state systems

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x17712661 ◽

2017 ◽

Vol 231 (5) ◽

pp. 516-533 ◽

Cited By ~ 3

Author(s):

Josquin Foulliaron ◽

Laurent Bouillaut ◽

Patrice Aknin ◽

Anne Barros

Keyword(s):

Complex Systems ◽

Bayesian Networks ◽

Bayesian Network ◽

Multicomponent System ◽

Dynamic Bayesian Network ◽

Dynamic Bayesian Networks ◽

Remaining Useful Life ◽

Predictive Maintenance ◽

Discrete State ◽

Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Download Full-text