For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{D}_n, \mathscr{P}(\mathfrak{D}_n))$ equipped with the uniform probability. We define a random variable $Y_n$ by $Y_n(\sigma) = \varepsilon(\sigma)$.
Specify the distribution of $Y_n$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{D}_n, \mathscr{P}(\mathfrak{D}_n))$ equipped with the uniform probability. We define a random variable $Y_n$ by $Y_n(\sigma) = \varepsilon(\sigma)$.
Calculate, for all $\varepsilon \in \{-1, 1\}$, $\lim_{n \rightarrow +\infty} \mathbb{P}(Y_n = \varepsilon)$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{S}_{n}, \mathscr{P}(\mathfrak{S}_{n}))$ equipped with the uniform probability. We define a random variable $Z_{n}$ by $Z_{n}(\sigma) = \nu(\sigma)$.
Explicitly state the distribution of $Z_{n}$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{S}_{n}, \mathscr{P}(\mathfrak{S}_{n}))$ equipped with the uniform probability. We define a random variable $Z_{n}$ by $Z_{n}(\sigma) = \nu(\sigma)$.
Calculate, for any natural integer $k \leqslant n$, $\lim_{n \rightarrow +\infty} \mathbb{P}\left(Z_{n} = k\right)$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{S}_{n}, \mathscr{P}(\mathfrak{S}_{n}))$ equipped with the uniform probability. We define a random variable $Z_{n}$ by $Z_{n}(\sigma) = \nu(\sigma)$.
Determine the average number of fixed points of a random permutation and its limit as $n$ tends to $+\infty$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{S}_n, \mathscr{P}(\mathfrak{S}_n))$ equipped with the uniform probability. We define a random variable $Z_n$ by $Z_n(\sigma) = \nu(\sigma)$.
Specify the distribution of $Z_n$.

For $n$ a natural integer greater than or equal to 2, we consider the probability space $(\mathfrak{S}_n, \mathscr{P}(\mathfrak{S}_n))$ equipped with the uniform probability. We define a random variable $Z_n$ by $Z_n(\sigma) = \nu(\sigma)$.
Calculate, for any natural integer $k \leqslant n$, $\lim_{n \rightarrow +\infty} \mathbb{P}(Z_n = k)$.

Let $n$ be a non-zero natural integer. For any permutation $\sigma \in \mathfrak{S}_{n}$, we recall that there exists, up to order, a unique decomposition $\sigma = c_{1} c_{2} \cdots c_{\omega(\sigma)}$, where $\omega(\sigma) \in \mathbb{N}^{*}$ where $c_{1}, \ldots, c_{\omega(\sigma)}$ are cycles with disjoint supports of respective lengths $\ell_{1} \leqslant \ell_{2} \leqslant \cdots \leqslant \ell_{\omega(\sigma)}$ and $\ell_{1} + \ell_{2} + \cdots + \ell_{\omega(\sigma)} = n$. We thus obtain a map $\omega : \mathfrak{S}_{n} \rightarrow \mathbb{N}$. For an integer $k$ at most $n$, we denote by $s(n,k)$ the number of permutations of $\mathfrak{S}_{n}$ such that $\omega(\sigma) = k$. We then consider, on the probability space $(\mathfrak{S}_{n}, \mathscr{P}(\mathfrak{S}_{n}))$ equipped with the uniform probability, the random variable $X_{n}$ defined by $X_{n}(\sigma) = \omega(\sigma)$.
Calculate, for $n \in \{2, 3, 4\}$, the quantity $\frac{1}{n!} \sum_{\sigma \in \mathfrak{S}_{n}} \omega(\sigma)$.

We consider a sequence of random variables $(X_n : \Omega \longrightarrow \{-1,1\})_{n \in \mathbf{N}}$ defined on the same probability space $(\Omega, \mathscr{A}, P)$, taking values in $\{-1,1\}$, mutually independent and centered. Show that for every $n \in \mathbf{N}^*$, the random variable $\frac{1+X_n}{2}$ follows a Bernoulli distribution with parameter $\frac{1}{2}$.

We consider a sequence of random variables $(X_n : \Omega \longrightarrow \{-1,1\})_{n \in \mathbf{N}}$ defined on the same probability space $(\Omega, \mathscr{A}, P)$, taking values in $\{-1,1\}$, mutually independent and centered. For every $n \in \mathbf{N}^*$, we denote $S_n = \sum_{k=1}^n X_k$. We fix the integer $n \geqslant 1$. A path is any $2n$-tuple $\gamma = (\varepsilon_1, \cdots, \varepsilon_{2n})$ whose components $\varepsilon_k$ equal $-1$ or $1$. An equality index of a path is any integer $k \in \llbracket 1, 2n \rrbracket$ such that $\sum_{i=1}^k \varepsilon_i = 0$. For every integer $i$ between $1$ and $n$, the event $A_i$ is defined by: $$A_i = \left\{\omega,\; 2i \text{ is an equality index of } (X_1(\omega), \cdots, X_{2n}(\omega))\right\}.$$ Calculate the probability $\mathbf{P}(A_i)$, for every integer $i$ between $1$ and $n$.

We consider a sequence of random variables $(X_n : \Omega \longrightarrow \{-1,1\})_{n \in \mathbf{N}}$ defined on the same probability space $(\Omega, \mathscr{A}, P)$, taking values in $\{-1,1\}$, mutually independent and centered. For every $n \in \mathbf{N}^*$, we denote $S_n = \sum_{k=1}^n X_k$. Let $\ell \in \mathbf{Z}$ be an integer and $n \geqslant 1$ be another integer. By distinguishing the case where the integer $\ell - n$ is even or odd, calculate $\mathbf{P}(S_n = \ell)$.

We consider a sequence of random variables $(X_n : \Omega \longrightarrow \{-1,1\})_{n \in \mathbf{N}}$ defined on the same probability space $(\Omega, \mathscr{A}, P)$, taking values in $\{-1,1\}$, mutually independent and centered. For every $n \in \mathbf{N}^*$, we denote $S_n = \sum_{k=1}^n X_k$. We fix the integer $n \geqslant 1$. The random variable $N_n : \Omega \longrightarrow \mathbf{N}$ counts, for every $\omega \in \Omega$, the number of equality indices of the path $(X_1(\omega), \cdots, X_{2n}(\omega))$. For every integer $i$ between $1$ and $n$, the event $A_i$ is defined by: $$A_i = \left\{\omega,\; 2i \text{ is an equality index of } (X_1(\omega), \cdots, X_{2n}(\omega))\right\}.$$ Show that the random variable $N_n$ has finite expectation and that its expectation $\mathbb{E}(N_n)$ equals: $$\mathbb{E}(N_n) = \sum_{i=1}^n \frac{\binom{2i}{i}}{4^i}.$$ [Hint: one may express the variable $N_n$ using indicator functions associated with the events $A_i$.]

In an urn containing $n$ white balls and $n$ black balls, we proceed to draw balls without replacement, until the urn is completely empty. The draws are equally likely at each draw. For every integer $k$ between $1$ and $2n$, we say that the integer $k$ is an equality index if, after drawing the first $k$ balls without replacement, there remain as many black balls as white balls in the urn. We note that the integer $2n$ is always an equality index. We denote by $M_n$ the random variable counting the number of equality indices $k$ between $1$ and $2n$.
By using for example the events $B_i$: ``the integer $i$ is an equality index'', show that the variable $M_n$ has finite expectation equal to: $$\mathbb{E}(M_n) = \sum_{i=0}^{n-1} \frac{\binom{2i}{i} \cdot \binom{2n-2i}{n-i}}{\binom{2n}{n}}.$$

For $N = 1$, among random variables with usual distributions, give without justification one example of a random variable satisfying (8) and two examples of random variables not satisfying (8), where (8) states $\mathbb{P}(|X_n| \leq K) = 1$ for some constant $K \geq 1$, with $\mathbb{E}[X_n] = 0$ and $\operatorname{Var}(X_n) \leq 1$.

For all $N \geq 1$, give an example of random variables satisfying hypotheses $$\mathbb{P}(|X_n| \leq K) = 1, \quad \mathbb{E}[X_n] = 0, \quad \operatorname{Var}(X_n) \leq 1$$ and such that $\mathbb{P}(|S_N| \geq N) \geq 1/2$, where $S_N := X_1 + \cdots + X_N$.

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Show that $$\mathbf { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) = \sum _ { i = 1 } ^ { n } c _ { i } ^ { 2 } .$$

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Deduce that there exists a real $\beta _ { p } > 0$ such that $$\mathbf { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { p } \right) ^ { 1 / p } \leq \beta _ { p } \mathbf { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) ^ { 1 / 2 } .$$

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Suppose $p \geq 2$. Show that $$\mathbf { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) ^ { 1 / 2 } \leq \mathbf { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { p } \right) ^ { 1 / p }$$

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Assume $1 \leq p < 2$. Justify that there exists $\theta \in ] 0,1 [$ such that $\frac { 1 } { 2 } = \frac { \theta } { p } + \frac { 1 - \theta } { 4 }$.

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Assume $1 \leq p < 2$. Show that $$\mathbf { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) \leq \mathbf { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { p } \right) ^ { 2 \theta / p } \mathbf { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { 4 } \right) ^ { ( 1 - \theta ) / 2 } .$$

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Assume $1 \leq p < 2$. Show that there exists $\tilde { \alpha } _ { p } > 0$ such that $$\tilde { \alpha } _ { p } \mathrm { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) ^ { 1 / 2 } \leq \mathbf { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { p } \right) ^ { 1 / p } .$$

Let $p \in \left[ 1 , + \infty \right[$. Let $\left( X _ { i } \right) _ { i \in \llbracket 1 , n \rrbracket}$ be a sequence of independent random variables all following a Rademacher distribution. Let $\left( c _ { 1 } , \ldots , c _ { n } \right) \in \mathbf { R } ^ { n }$. Deduce that there exists a real $\alpha _ { p }$ such that $$\alpha _ { p } \mathrm { E } \left( \left( \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right) ^ { 2 } \right) ^ { 1 / 2 } \leq \mathrm { E } \left( \left| \sum _ { i = 1 } ^ { n } c _ { i } X _ { i } \right| ^ { p } \right) ^ { 1 / p } .$$

Let $\left( X _ { i } \right) _ { i \in \mathbf { N } }$ be a sequence of independent random variables all following a Rademacher distribution. Show that the map $\varphi$ defined on $\left( L ^ { 0 } ( \Omega ) \right) ^ { 2 }$ by $$\forall X , Y \in L ^ { 0 } ( \Omega ) , \quad \varphi ( X , Y ) = \mathbf { E } ( X Y )$$ is an inner product on $L ^ { 0 } ( \Omega )$.

Let $( s , i , r ) \in E$ where $E = \{ ( s , i , r ) \in \mathbf{N}^3,\, s + i + r = M \}$. Conditional on the event $\left( \left( \tilde { S } _ { n } , \tilde { I } _ { n } , \tilde { R } _ { n } \right) = ( s , i , r ) \right)$, what is the probability, denoted $p ( i )$, for a susceptible person to be infected during this day?
Each of the $s$ healthy persons meets, independently of the others, $K$ persons chosen at random from the $M$ persons in the total population. As soon as at least one of the meetings is with an infected person, the healthy person becomes infected the next morning.

Establish the following identity:
$$\mathbf { E } \left[ \Delta \tilde { R } _ { n } \right] = \rho \mathbf { E } \left[ \tilde { I } _ { n } \right]$$
where each infected person recovers at the end of the day with probability $\rho \in ]0,1[$, independently of others, and $\Delta \tilde{R}_n = \tilde{R}_{n+1} - \tilde{R}_n$.