
Concept explainers
Explanation of Solution
Maximizing the expected profits:
Consider W is the event that represents the weather is warm and C is the event that represent the weather is cold.
Also, assume that Cp is the event that represents the expert predicts the weather is cold and Wp is the event that represents the expert predicts the weather is warm.
States of nature | ||
Strategies | Warm (W) | Cold (C) |
Wheat | 7000 | 6500 |
Corn | 8000 | 5000 |
Probability | 0.6 | 0.4 |
There is 90% of chance that the expert predicts year is cold and it is actually cold, i.e., conditional probability which is given as follows:
P(Cp|C) = 0.90
Therefore, the probability that the weather is predicted as warm when it was cold is given as follows:
P(Wp|C) = 1 - P(Cp|C)
= 1 – 0.9
= 0.10
Similarly, it is given that 80% of times the weather is actually warm and the expert also predicted the same, i.e.,
P(Wp|W) = 0.80
Also, P(Cp|W) = 0.20
Calculate the probability that an expert predicts correctly or incorrectly. Let, CP and ICP denote the events correct prediction and incorrect prediction by expert. Therefore, the probabilities are as follows:
P(CP) = P(Wp|W) x P(W) + P(Cp|C) x P(C)
= (0.80 x 0.60) + (0.9 x 0.40)
= 0.48 + 0.36
= 0.84
P(CP) = P(Cp|W) x P(W) + P(Wp|C) x P(C)
= (0...
Explanation of Solution
Calculating EVSI:
Expected Value of Sample Information (EVSI) is referred to as the value of information that would be obtained from sample information. For this, first determine the expected value with the sample information (EVWSI). This is the sum of the expected value when weather forecast is performed plus the cost of weather forecast.
EVWSI = (Expected value) + (Cost of hiring expert)
= 6259.4 + 600
= 6859.4
The EVWOI value is 6800. Therefore,
EVSI = EVWSI – EVWOI
= 6859.4 – 6800
= $59...

Trending nowThis is a popular solution!

Chapter 13 Solutions
Operations Research : Applications and Algorithms
- Need help with coding in this in python!arrow_forwardIn the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder…arrow_forward
- Why I need ?arrow_forwardHere are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…arrow_forward
- Operations Research : Applications and AlgorithmsComputer ScienceISBN:9780534380588Author:Wayne L. WinstonPublisher:Brooks ColeC++ Programming: From Problem Analysis to Program...Computer ScienceISBN:9781337102087Author:D. S. MalikPublisher:Cengage LearningProgramming Logic & Design ComprehensiveComputer ScienceISBN:9781337669405Author:FARRELLPublisher:Cengage
- C++ for Engineers and ScientistsComputer ScienceISBN:9781133187844Author:Bronson, Gary J.Publisher:Course Technology PtrNp Ms Office 365/Excel 2016 I NtermedComputer ScienceISBN:9781337508841Author:CareyPublisher:Cengage


