# Maximum Likelihood problem in book Pattern Recognition and Machine Learning

I ran into a problem in section 2.4.1 namely "Maximum likelihood and sufficient statistics" under "Exponential Distribution Family" of Bishop's Pattern Recognition and Machine Learning. excerpt

How to derive from (2.195) to (2.224)?What am I missing?

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Interchange integration and differentiation. Then just as you would do if η were a scalar d/dη exp(η u(x)) =u(x) exp(η u(x)). –  Michael Chernick Jun 4 '12 at 13:51