# What is the relation between “conjugate priors” and the approximate inference?

I know that "conjugate prior" is to help us calculate the the denominator of the Bayes formula(to make the calculations easier). And I just learnt to approximate the inference by mean field approximation to help us calculate the denominator of the Bayes formula(make the calculations easier).

What is the relation between the two? Why do we need "mean field approximation" If we have a conjugate prior?