At some point, in Bishop's book 'Pattern recognition and Machine Learning', (p.75) he is talking about multinomial distributions in a classification context, introducing a suitable probability distribution $p(\bf x | \mu)$:
with given constraints for $\bf x$ and $\bf \mu$.
What I don't understand is why the distribution is normalized, i.e. equality 2.27. How does he achieve that?