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Let's assume we have an engine, that with 1/3 probability can stop working. I'll call this variable ENGINE.

If it stops working, then your car doesn't work. If the engine is working, then your car will work 99% of the time. I'll call this one CAR.

Now, if your car is old(OLD), instead of not working 1/3 of the time, your engine will stop working 1/2 of the time.

I'm being asked to first design the network and then assign all the conditional probabilities associated with the table.

I'd say the diagram of this network would be something like

OLD -> ENGINE -> CAR

Now, for the conditional probabilities tables I did the following:

OLD   |ENGINE

------------

True  | 0.50

False | 0.33

and

ENGINE|CAR

------------

True  | 0.99

False | 0.00

Now, I am having trouble with how to define the probabilities of OLD. In my point of view, old is not something that has a CAUSE relationship with ENGINE, I'd say it is more a characteristic of it. Maybe there is a different way to express this in the diagram? If the diagram is indeed correct, how would I go to make the tables?

## 1 Answer

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by (108k points)

If you want to apply the network for prediction, leaving the OLD variable in the model will allow for using that information when it is available and not using it when it is not. Especially if this network applies to cars in general. OLD is something like a parameter that is given from the outside world. Thus, it's not a random variable and possibly should not be part of your diagram.

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