00:01
Once again, welcome to a new problem.
00:04
This time we're dealing with probability.
00:07
We're dealing with probability.
00:09
And when you think about probability, this is the quantification.
00:15
So this is the quantification of chance processes.
00:25
So this is the quantification of chance processes such that we have probability of heads.
00:31
Is one half and probability of tails is also one half and these two are equally likely events so these two are equally likely events and when you think about conditional probability so in the case of conditional probability i mean think about different entities.
01:09
So we have two entities here.
01:11
We have a and we have b and in the middle we have a and b.
01:17
So the conditional probability of a given b is going to be probability of a and b within the context of b.
01:28
So if you can see probability of a within b so this is within b that's why we have probability of a and b at the top, and then we have probability of b at the bottom.
01:45
And so that's why we're saying a given b.
01:48
So given b becomes the denominator for conditional probability that you're looking at.
01:58
So we have a new problem, and in this particular problem, we're given specifics, and we're using bayesian network.
02:09
So a, b, c, d, and these happen to be boolean random variables, and probability of a is 0 .75, and probability of c given a is 0 .7.
02:22
C given not a, this is the complement of a, is 0 .25, probability of b given a is 0 .2.
02:38
Probability of b given not a is 0 .5...