3
$\begingroup$

Th Bayes' rule specifies how an agent should update its belief in a proposition based on a new piece of evidence. Suppose an agent has a current belief in proposition $h$ based on evidence $k$ already observed, given by $P(h \mid k)$, and subsequently observes $e$.

In artificial intelligence, where can we use the Bayes' rule? I am unable to understand this concept.

$\endgroup$
2
$\begingroup$

A toy example:

Suppose an agent has information about the reliability of fire alarms. It may know how likely it is that an alarm will work if there is a fire i.e. $P(alarm | fire)$. But, if the problem is tweaked a bit and if the agent must know the probability that there is a fire, given that there is an alarm, it can use Bayes' rule:

$P(fire | alarm) = [P(alarm | fire) ×P(fire )] / P(alarm )$

where:

$P(alarm | fire)$ is the probability that the alarm worked, assuming that there was a fire. It is a measure of the alarm's reliability.

The expression $P(fire)$ is the probability of a fire given no other information. It is a measure of how fire-prone the building is. 

$P(alarm)$ is the probability of the alarm sounding, given no other information.

$\endgroup$
0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.