Quote Originally Posted by Kival View Post
Pr(E | H) = Pr(H ∩ E)/Pr(H) (Definition of baysian probability)
=> Pr(H ∩ E) = Pr(E | H)*Pr(H) (just simple transformation)

As Pr(E | H) has a value range in the interval [0;1] (it's a probability function), obviously it follows:

Pr(H ∩ E) <= Pr(E | H)*Pr(H)

Because of Pr(H | E)=Pr(H ∩ E)/Pr(E), the second part of a) follows trivially.
From the above you can agree on:

Pr(H ∩ E) = Pr(E | H)*Pr(H) ?

Now just remember, what you know about the Pr-function. It's a function which gives you the probabilty of an event. The event is described as a set or as a logical combination of events. What do you know in general about the probability function? It has only values between 0 and 1. So 0<=Pr(A)<=1 for all possible events A. In mathematical detail, Pr(. | B), the baysian probability function is an own function but it's still a probability function and so it still has only values from 0 to 1. If you multiply Pr(H) with a value between 0 and 1, it can only be Pr(H) or smaller.