Bayesian networks tutorial with genie
- 1. MSIM 410/510 Model Engineering GeNIe for Bayesian Networks Gornto 221 2:45-4:00pm
- 2. GeNIe | Introduction GeNIe – Graphical Network Interface – can be used to construct and simulate Bayesian and Decision networks • Graphically represent probability results • Predict probability of an outcome and results of related variables GeNIe Interface
- 3. GeNIe | Download Installation Files – On Blackboard Under Modules --> Modules 6: Statistical Based Modeling --> GeNIe 2. . 1 Download
- 4. GeNIe | GUI Chance Deterministic Equation Decision Value Connector Textbox Run/Simulate Main Tools
- 5. GeNIe | Bayesian Network Example Sprinkler Raining Wet Grass P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 P(S=T) P(S=F) 0.2 0.8 P(R=T) P(R=F) 0.4 0.6 S R P(W=T|S, R) P(W=F|S, R) T T 0.95 0.05 T F 0.90 0.10 F T 0.90 0.10 F F 0.10 0.90
- 6. GeNIe | Bayesian Network Example Sprinkler Raining Wet Grass P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler On 0.2 Sprinkler Off 0.8 Raining 0.4 Not Raining 0.6 Sprinkler Sprinkler On Sprinkler Off Raining Raining Not Raining Raining Not Raining Wet Grass 0.95 0.9 0.9 0.1 Dry Grass 0.05 0.1 0.1 0.9
- 7. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Use the “Chance” Tool to create three “Chances” Sprinkler Wet Grass
- 8. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass Use the “Arc” Tool to connect
- 9. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass Double click on the “Sprinkler Chance”, go to the “Definition” tab and change the two states as the ones shown in the figure above.
- 10. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass Double click on the “Raining Chance”, go to the “Definition” tab and change the two states as the ones shown in the figure above.
- 11. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass Double click on the “Wet Grass Chance”, go to the “Definition” tab and change the two states as the ones shown in the figure above.
- 12. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass The probability that the grass is wet?
- 13. GeNIe | Bayesian Network Example 1. Run the simulation 2. Check the results by double clicking the “Wet Grass” chance then go to the “Value” tab
- 14. GeNIe | Bayesian Network Example Raining P(S) = 0.2 P(R) = 0.4 P(W|R, S) = 0.95 P(W|R, ~S) = 0.90 P(W|~R, S) = 0.90 P(W|~R, ~S) = 0.10 Sprinkler Wet Grass Given that the sprinkler is on, what is the probability that the grass is wet?
- 15. GeNIe | Bayesian Network Example Since we know that the sprinkler is on, we need to set “Sprinkler On” as the evidence. • Right click on the little check mark of the “Sprinkler” chance, “Set Evidence” - > “SprinklerON”
- 16. GeNIe | Bayesian Network Example 1. Run the simulation again 2. Check the results by double clicking the “Wet Grass” chance then go to the “Value” tab
- 17. GeNIe | Decision Network Example
- 18. GeNIe | Decision Network Example An Expected Utility “score” is to be determined for each possible decision outcome, and the Highest Expected Utility points to the “best” decision that can be made. Calculating Highest Expected Utility: First calculate Expected Utility (EU) for: if John decides to pay for the boat P(FC=Y) * U(FC|JP=Y) + P(FC=N) * U(FC=Y|JP=Y) = (0.4 * 0.55 + 0.6 * 0.25) * 100 + (0.4 * 0.45 + 0.6 * 0.75) * -50 = 5.5 Next calculate EU for: if John decides to Not pay for the boat P(FC=Y) * U(FC|JP=N) + P(FC=N) * U(FC=N|JP=N) = (0.4 * 0.55 + 0.6 * 0.25) * 25 + (0.4 * 0.45 + 0.6 * 0.75) * -25 = -6.5 The Highest Expected Utility is derived from the case where John decides to pay for the Boat, so that will be his decision.
- 19. GeNIe | Decision Network Example Chance Chance Value Decision
- 20. GeNIe | Decision Network Example
- 21. GeNIe | Decision Network Example
- 22. GeNIe | Decision Network Example
- 23. GeNIe | Decision Network Example
- 24. GeNIe | Decision Network Example if John decides to pay for the boat
- 25. GeNIe | Decision Network Example if John decides to not pay for the boat
- 26. GeNIe | Decision Network Example Burglary (B) and earthquake (E) directly affect the probability of the alarm (A) going off, but whether or not John calls (J) or Mary calls (M) depends only on the alarm. Your tasks: 1. Construct this network using GeNIe. 2. Investigate these two questions by running the simulation in GeNIe a. What is the probability that there is a burglary given that John calls? b. What about if Mary also calls right after John hangs up? 3. Record the results from GeNIe by using screenshot and paste them to a word doc. Turn in both your model file and doc file on Blackboard by the end of class. Please do not submit .zip or .rar files.
- 27. The End