I find it easy to understand with following e.g. H = Heights of Student, W = Weights of Students. If its given W =w, Revised model consist of only those students that have weights w and height any of h values . In revised model, P(H=h) = P (Picking students that have height = h). If you find P (h = hi) for each value of height, it will cover all the students so probability is 1.
I find it easy to understand with following e.g. H = Heights of Student, W = Weights of Students. If its given W =w, Revised model consist of only those students that have weights w and height any of h values . In revised model, P(H=h) = P (Picking students that have height = h). If you find P (h = hi) for each value of height, it will cover all the students so probability is 1.
Very clear explanation, thank you MIT!
LOVE YOU MIT!!
very clear and understandable MIT
Thanks MIT
conditional pmf 2:05
what would be the difference of PMF conditioned by an event with a PMF conditioned by another random variable?
6:00 7:35