# Does P(E|H) > P(E) => P(H | E) > P(H) explain correlation between bayesian and frequentialist results?

I have just noted in the Ross book that given P(E|H) > P(E)

If the occurrence of B makes A more likely, does the occurrence of A make B more likely?

we can conclude that P(E|H) / P(E) > 1 and, because P(H | E) P(E) = P(E|H)P(H) it follows that P(H | E) /P(H) = P(E|H)/P(E) > 1, which says that P(H | E) /P(H) > 1 or P(H | E) > P(H). That is, if evidence has higher chances in the column that used by frequentist then hypothesis will also enjoy the higher probability in the bayesian table. It seems that there is a correlation between two types of inferences. Why is correlation not absolute, why are the differences, can you say that in the broad lines?