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Tom's Feb 16 edits of two lectures
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lectures/likelihood_ratio_process.md

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@@ -232,7 +232,7 @@ Mathematical induction implies
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$E\left[L\left(w^{t}\right)\bigm|q=g\right]=1$ for all
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$t \geq 1$.
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## Peculiar Property of Likelihood Ratio Process
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## Peculiar Property
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How can $E\left[L\left(w^{t}\right)\bigm|q=g\right]=1$ possibly be true when most probability mass of the likelihood
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ratio process is piling up near $0$ as
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A Navy Captain who had been ordered to perform tests of this kind had doubts about it that he
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presented to Milton Friedman, as we describe in {doc}`this lecture <wald_friedman>`.
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## Kullback–Leibler divergence
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## Kullback–Leibler Divergence
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Now let’s consider a case in which neither $g$ nor $f$
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generates the data.

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