Why Gauss distribution is a necessary condition for the equality of maximum likelihood estimate and sample mean

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I'm reading Probability Theory: The Logic of Science by Jaynes. And while I'm reading section 7.3, he proofed that Gauss distribution is a necessary condition for the equality of maximum likelihood estimate and sample mean.

But he just derived Gauss distribution from a special case of sample mean, how does that imply a necessary condition? It is like if we know a + b = 0,then we assume a = 1, b = -1, from this special case we get a - b = 2, but that doesn't implies a - b = 2 is a necessary condition of a + b = 0.

( You may read Chapter 7 of this book online if you don't have this book).