Topic: Large deviations, in particular: Sanov's theorem
Let $\Sigma$ be a Polish space and $M_1(\Sigma)$ be the space of probability measures in $\Sigma$. Prove that
$$\left\lVert\nu\mu\right\rVert_{var}^2\leq 2\textbf{H}(\nu\mu), \ \ \ \ \mu,\nu\in\textbf{M}_1(\Sigma).\tag{*}$$
A proof of (*) can be based on the observation that
$$3(x1)^2\leq(4+2x)(x\log xx+1),\ \ \ \ x\in[0,\infty),$$
the fact that $\left\lVert\nu\mu\right\rVert_{var}=\left\lVert f1\right\rVert_{L^1(\mu)}$ if $\nu <<\mu$ and $f=\frac{d\nu}{d\mu}$, and Schwartz's inequality.
notes:
$\textbf{H}(\nu\mu)=\left\{ \begin{array}{rcl} \int_\Sigma f\log f\ d\mu & if\ \ \nu\ll\mu\ and\ \ f=\frac{d\nu}{d\mu}\\ \infty & otherwise \end{array}\right.$
side note: $\int_\Sigma f\log f\ d\mu=\int_\Sigma \log f\ d\nu$
$\left\lVert\alpha\right\rVert_{var}=\sup\bigg\{\int\phi\ d\alpha:\phi\in C_b(\Sigma;\mathbb{R})\ with\ \left\lVert\phi\right\rVert_{C_b}\leq 1\bigg\}$
is the (total) variation norm (that is the definition in large deviation book written by jeandominique deuschel and daniel w. stroock)
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damn, it is perfect (unless i miss something) thank you very much.
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What is H?
H is relative entropy
Can you upload the definition of conditional entropy as well? Different textbooks use slightly different definitions. Does "var" norm means the total variation norm?
I added some definition to the question, and by the way, I need the answer in like 15 hours from now, I accidentally added the extra time
Never mind, I found the solution