Vector = Stochastic Matrix * Arbitrary Vector
Keep multiplying as above and eventually, the vector doesnt change anymore. The vector then is the eigen vector of the stochastic matrix with eigenvalue of 1.
Conditionalization
P(A|B) = P(A & B)/P(B)
Marginalization
P(B) = Sum {P(B|A) P(A)} over all values of A
Baye's Rule:
It enables us to reverse probabilities.
P(A|B) = P(B|A)*P(A)/P(B)