# 统计代写|贝叶斯网络代写Bayesian network代考|BNP2022

## 统计代写|贝叶斯网络代写Bayesian network代考|Risk Analysis When Finding Evidence

When an accident occurs, its most probable cause must be determined. In the model, the probahilities of state “Yes” of the nodes “Fire,” “ToxicGas,” and “TaCrack” are changed to $100 \%$, which means they simulate the accidents happening. The probabilities of other nodes change accordingly. Figure 9 shows the probabilities during a fire. Nodes with multiple changes in probabilities changed are circled in red. Table 8 shows the probabilities of several nodes. Underlined numbers represent prior probabilities.

Figure 10 shows the probabilities changing of several basic nodes when finding different evidence. For example, when “Fire” happens, “InsExperience”‘s probability increases the most. Thus, it is the most likely cause of the fire. When “ToxicGas” occurs, “InsExperience”‘s probability significantly increases. “ShiftChange” also increases considerably and hence can be a possible and primary reason of “ToxicGas.” Similarly, when “TaCrack” happens, “InsExperience,” “ShiftChange,” and “Aging” can be considered first as the possible reasons.

Figure 11 shows the probabilities changing of several child nodes when finding different evidence. When “Fire,” “ToxicGas,” or “TaCrack” happens, “HumanErr” and “EquipmentFail” are the most responsible. When “ToxicGas” happens, the probability of “EquipmentFail” increases the most. Group error also has a relatively high probability responsible for the happening of “ToxicGas” and “TaCrack.”

## 统计代写|贝叶斯网络代写Bayesian network代考|Possible Accident When an Error Occurs

Risk is best avoided by increasing reliability. However, mistakes are sometimes inevitable, and when an error occurs, people suffer adverse consequences. Simulation can show the situation in which people know that an error has happened and which accident is most likely to happen. In this case, operators can implement remedial measures to reduce risk. Table 11 gives the probability of several nodes when an error occurs. Underlined numbers represent the prior probabilities of identified causes.
Figures 16 and 17 compare and present the probabilities of these nodes to change with the causes. The different column colors in the bar chart represent various identified causes (“HumanErr,” “OrganizationErr,” and “EquipmentFail”). The node names are on the horizontal coordinate, whereas the probabilities are on the vertical coordinate. In Fig. 16, the sixth column shows the occurrence of a human error (The probability of state “Yes” of “HumanErr” is $100 \%$.). The probability of operators to become fatigued (state “Yes” of “Fatigue”) is $12.9 \%$.

Figure 16 shows the probability changing of certain basic nodes when an error happens. When “HumanErr” happens, “InsExperience” and “ShiftChange” increase the most; this means that they are the most likely reasons to result in human error. When “EquipmentFail” happens, “Damage” and “Aging” increase the most; “PowerCut” also increases. These observations indicate these three events can be possible reasons for equipment failure.

Figure 17 shows the probabilities changing of certain child nodes when an error transpires. When “HumanErr” happens, “EquipmentFail” increases correspondingly. Human error may lead to equipment failure, according to the relationship shown in Fig. $8 .$

Table 11 shows that when “HumanErr” occurs, the probability of “Fire” increases from $0.31$ to $1.46 \%$. “SpoIgnition,” which increases from $0.95$ to $10.9 \%$, shows the most noticeable change. Therefore, when a human error happens, the temperature should be given special attention to prevent fire and spontaneous ignition.

## 统计代写|贝叶斯网络代写贝叶斯网络代考|当错误发生时可能发生的事故

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