politely take this task seriously and clear writing ifpossible

Question 2 The structure, or topology, of the network, should capture qualitative relationships between variables. In particular, two nodes should be connected directly if one affects or causes the other, with the arc indicating the direction of the effect. Therefore, in the medical diagnosis, they might ask what factors affect a patient’s chance of having cancer? If the answer is “Pollution and smoking,” then we should add arcs from Pollution and Smoker to Cancer. Similarly, having cancer will affect the patient’s breathing and the chances of having a positive X-ray result Therefore, they add arcs from Cancer to Dyspnoea and XRay. The consequential structure is shown in Figure 2.11. Can you find, if the Dyspnoea occurred due to Smoker or not base on Bayesian Network? P(S-T) Pollution Smoker 0.30 P(P=L) P S P(CTIPS) 0.90 Cancer |нт 0.05 H F 0.02 L T 0.03 Dyspnoca XRay L F 0.001 с РХ-роsC) C P(D TIC) 0.65 Т 0.90 Т 0.20 0.30 Figure 2.1 A Bayesian Network for the lung cancer problem https://bayesian-intelligence.com/publications/bai/book/BAI Chapter2.pdf Show transcribed image text Question 2 The structure, or topology, of the network, should capture qualitative relationships between variables. In particular, two nodes should be connected directly if one affects or causes the other, with the arc indicating the direction of the effect. Therefore, in the medical diagnosis, they might ask what factors affect a patient’s chance of having cancer? If the answer is “Pollution and smoking,” then we should add arcs from Pollution and Smoker to Cancer. Similarly, having cancer will affect the patient’s breathing and the chances of having a positive X-ray result Therefore, they add arcs from Cancer to Dyspnoea and XRay. The consequential structure is shown in Figure 2.11. Can you find, if the Dyspnoea occurred due to Smoker or not base on Bayesian Network? P(S-T) Pollution Smoker 0.30 P(P=L) P S P(CTIPS) 0.90 Cancer |нт 0.05 H F 0.02 L T 0.03 Dyspnoca XRay L F 0.001 с РХ-роsC) C P(D TIC) 0.65 Т 0.90 Т 0.20 0.30 Figure 2.1 A Bayesian Network for the lung cancer problem https://bayesian-intelligence.com/publications/bai/book/BAI Chapter2.pdf

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