Sensitivity Analysis in a Bayesian Network for Modeling an Agent

2018 ◽  
pp. 65-76
Author(s):  
Yoko Ishino
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network

Sensitivity Analysis of a Baysian Network Model on PCM Equipments

2013 ◽  
Vol 391 ◽  
pp. 544-547
Author(s):  
Hui Sheng Gao ◽  
Xu Rui Wang
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Physical System ◽  
Communication Systems ◽  
Focus Of Attention ◽  
Cyber Physical System ◽  
Pulse Code Modulation ◽  
Counter Measures ◽  
Security Problem ◽  
Code Modulation

In power communication systems, the pulse code modulation (PCM) equipment play an important role. Its security has been a focus of attention, when the concept of cyber physical system is proposed. In order to solve the security problem of PCM equipments, a Bayesian Network (BN) model is used in this paper. By analyzing the sensitivity of BN model, we can get the influence of each input variable to outcome variables. For illustration, four PCM equipments are selected from some substations. They are utilized to show the feasibility of the BN model in evaluating the security of PCM equipments and the sensitivity analysis. Empirical results show that some effective counter measures can be found to help decision maker improve the security of PCM equipments. The BN model can effectively evaluate the security of PCM equipments. After analyzing the sensitivity, the most reasonable and effective countermeasures are advanced.

scholarly journals Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Niamat Ullah Ibne Hossain ◽  
Farjana Nur ◽  
Raed Jaradat ◽  
Seyedmohsen Hosseini ◽  
Mohammad Marufuzzaman ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Performance Index ◽  
Belief Propagation ◽  
Transportation System ◽  
Maritime Transportation ◽  
Economic Success ◽  
Inland Waterway ◽  
Overall Performance ◽  
Port Economics

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports.

scholarly journals Questionnaire Investigation on Tourists’ Behavior and its Sensibility Analysis Utilizing Bayesian Network

2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Local Government ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
Izu Peninsula ◽  
Regional Collaboration ◽  
Cherry Blossom ◽  
Sensibility Analysis ◽  
Tourism Service

Tourists from abroad are increasing rapidly in Japan. Kawazu town in Izu Peninsula is famous for its cherry trees. In the cherry blossom season, many tourists visit this town. In order to get much more visitors, tourists’ behavior should be investigated much further. The Kawazu Cherry Blossom Festival was carried out in February 2015. Our research investigation was performed during that period. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. In this research, we construct the model utilizing Bayesian Network and causal relationship is sequentially chained by the characteristics of travelers, an objective to visit Izu Peninsula in Japan and the main occasion to visit them. We analyzed them by sensitivity analysis and some useful results were obtained. Sensitivity analysis is performed by back propagation method. We have presented the paper concerning this. But the volume becomes too large, therefore we have split them and this paper shows the latter half of the investigation result by setting evidence to Bayesian Network items. These are utilized for constructing a much more effective and useful tourism service. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals Sensitivity Analysis of a Bayesian Network

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Chenzhao Li ◽  
Sankaran Mahadevan
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Prior Distribution ◽  
Global Sensitivity Analysis ◽  
Auxiliary Variable ◽  
Sensitivity Index ◽  
Main Concern ◽  
Deterministic Function ◽  
Low Sensitivity ◽  
Higher Sensitivity

In a Bayesian network (BN), how a node of interest is affected by the observation at another node is a main concern, especially in backward inference. This challenge necessitates the proposed global sensitivity analysis (GSA) for BN, which calculates the Sobol’ sensitivity index to quantify the contribution of an observation node toward the uncertainty of the node of interest. In backward inference, a low sensitivity index indicates that the observation cannot reduce the uncertainty of the node of interest, so that a more appropriate observation node providing higher sensitivity index should be measured. This GSA for BN confronts two challenges. First, the computation of the Sobol’ index requires a deterministic function while the BN is a stochastic model. This paper uses an auxiliary variable method to convert the path between two nodes in the BN to a deterministic function, thus making the Sobol’ index computation feasible. Second, the computation of the Sobol’ index can be expensive, especially if the model inputs are correlated, which is common in a BN. This paper uses an efficient algorithm proposed by the authors to directly estimate the Sobol’ index from input–output samples of the prior distribution of the BN, thus making the proposed GSA for BN computationally affordable. This paper also extends this algorithm so that the uncertainty reduction of the node of interest at given observation value can be estimated. This estimate purely uses the prior distribution samples, thus providing quantitative guidance for effective observation and updating.

scholarly journals Sensitivity Study on Availability of I&C Components Using Bayesian Network

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Rahman Khalil Ur ◽  
Jinsoo Shin ◽  
Muhammad Zubair ◽  
Gyunyoung Heo ◽  
Hanseong Son
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Circuit Breaker ◽  
Sensitivity Study ◽  
High Impact ◽  
System Risk ◽  
Pressure Transmitter ◽  
And Control ◽  
Reactor Protection System ◽  
The Impact

The objective of this study is to find out the impact of instrumentation and control (I&C) components on the availability of I&C systems in terms of sensitivity analysis using Bayesian network. The analysis has been performed on I&C architecture of reactor protection system. The analysis results would be applied to develop I&C architecture which will meet the desire reliability features and save cost. RPS architecture unavailabilityP(x=0)and availabilityP(x=1)were estimated to6.1276E-05and9.9994E-01for failure (0) and perfect (1) states, respectively. The impact of I&C components on overall system risk has been studied in terms of risk achievement worth (RAW) and risk reduction worth (RRW). It is found that circuit breaker failure (TCB), bi-stable processor (BP), sensor transmitter (TR), and pressure transmitter (PT) have high impact on risk. The study concludes and recommends that circuit breaker bi-stable processor should be given more consideration while designing I&C architecture.

Confidence Interval of Bayesian Network and Global Sensitivity Analysis

AIAA Journal ◽  
2017 ◽  
Vol 55 (11) ◽  
pp. 3916-3924 ◽  
Author(s):  
Sangjune Bae ◽  
Nam H. Kim ◽  
Chanyoung Park ◽  
Zaeill Kim
Keyword(s):  
Sensitivity Analysis ◽  
Confidence Interval ◽  
Bayesian Network ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity
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