Let $n \geqslant 1$ be a natural integer, and let $\left( X _ { 1 } , \ldots , X _ { n } \right)$ be mutually independent discrete real random variables such that, for all $k \in \{ 1 , \ldots , n \}$, $$P \left[ X _ { k } = 1 \right] = P \left[ X _ { k } = - 1 \right] = \frac { 1 } { 2 }$$ We define $$S _ { n } = \frac { 1 } { n } \sum _ { k = 1 } ^ { n } X _ { k }$$ as well as, for all $\lambda \in \mathbb { R }$, $$\psi ( \lambda ) = \log \left( \frac { 1 } { 2 } e ^ { \lambda } + \frac { 1 } { 2 } e ^ { - \lambda } \right)$$ Show that for all $t \in \mathbb { R }$, we have $$\frac { 1 } { n } \log P \left[ S _ { n } \geqslant t \right] \leqslant \inf _ { \lambda \geqslant 0 } ( \psi ( \lambda ) - \lambda t )$$
Let $n \geqslant 1$ be a natural integer, and let $\left( X _ { 1 } , \ldots , X _ { n } \right)$ be mutually independent discrete real random variables such that, for all $k \in \{ 1 , \ldots , n \}$,
$$P \left[ X _ { k } = 1 \right] = P \left[ X _ { k } = - 1 \right] = \frac { 1 } { 2 }$$
We define
$$S _ { n } = \frac { 1 } { n } \sum _ { k = 1 } ^ { n } X _ { k }$$
as well as, for all $\lambda \in \mathbb { R }$,
$$\psi ( \lambda ) = \log \left( \frac { 1 } { 2 } e ^ { \lambda } + \frac { 1 } { 2 } e ^ { - \lambda } \right)$$
Show that for all $t \in \mathbb { R }$, we have
$$\frac { 1 } { n } \log P \left[ S _ { n } \geqslant t \right] \leqslant \inf _ { \lambda \geqslant 0 } ( \psi ( \lambda ) - \lambda t )$$