Consider the network for car diagnosis shown in
Figure car-starts-figure
. 1. Extend the network with the Boolean variables ${IcyWeather}$ and ${StarterMotor}$.
2. Give reasonable conditional probability tables for all the nodes.
3. How many independent values are contained in the joint probability distribution for eight Boolean nodes, assuming that no conditional independence relations are known to hold among them?
4. How many independent probability values do your network tables contain?
5. The conditional distribution for ${Starts}$ could be described as a noisy-AND distribution. Define this family in general and relate it to the noisy-OR distribution.
. 1. Extend the network with the Boolean variables ${IcyWeather}$ and ${StarterMotor}$.
2. Give reasonable conditional probability tables for all the nodes.
3. How many independent values are contained in the joint probability distribution for eight Boolean nodes, assuming that no conditional independence relations are known to hold among them?
4. How many independent probability values do your network tables contain?
5. The conditional distribution for ${Starts}$ could be described as a noisy-AND distribution. Define this family in general and relate it to the noisy-OR distribution.
Consider the network for car diagnosis shown in
Figure car-starts-figure
.
1. Extend the network with the Boolean variables ${IcyWeather}$ and
${StarterMotor}$.
2. Give reasonable conditional probability tables for all the nodes.
3. How many independent values are contained in the joint probability
distribution for eight Boolean nodes, assuming that no conditional
independence relations are known to hold among them?
4. How many independent probability values do your network tables
contain?
5. The conditional distribution for ${Starts}$ could be described as
a noisy-AND distribution. Define this
family in general and relate it to the noisy-OR distribution.