概率论与数理统计 (Spring 2024)/Problem Set 3

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Assumption throughout Problem Set 3

Without further notice, we are working on probability space [math]\displaystyle{ (\Omega,\mathcal{F},\mathbf{Pr}) }[/math].

Without further notice, we assume that the expectation of random variables are well-defined.

The term [math]\displaystyle{ \log }[/math] used in this context refers to the natural logarithm.

Problem 1 (Warm-up Problems)

• [Variance (I)] Let [math]\displaystyle{ X_1,X_2,\cdots, X_n }[/math] be pairwise independent random variables. Show that [math]\displaystyle{ \textbf{Var}\left[\sum_{i=1}^n X_i\right] =\sum_{i=1}^n \textbf{Var} [X_i] }[/math].
• [Variance (II)] Each member of a group of [math]\displaystyle{ n }[/math] players rolls a (fair) die. For any pair of players who throw the same number, the group scores [math]\displaystyle{ 1 }[/math] point. Find the mean and variance of the total score of the group.
• [Variance (III)] An urn contains [math]\displaystyle{ n }[/math] balls numbered [math]\displaystyle{ 1, 2, \ldots, n }[/math]. We select [math]\displaystyle{ k }[/math] balls uniformly at random without replacement and add up their numbers. Find the mean and variance of the sum.
• [Variance (IV)] Let [math]\displaystyle{ N }[/math] be an integer-valued, positive random variable and let [math]\displaystyle{ \{X_i\}_{i=1}^{\infty} }[/math] be indepedently identically distributed random variables that are independent of [math]\displaystyle{ N }[/math], too. Precisely, for any finite subset [math]\displaystyle{ I \subseteq\mathbb{N}_+ }[/math], [math]\displaystyle{ \{X_i\}_{i \in I} }[/math] and [math]\displaystyle{ N }[/math] are mutually independent. Let [math]\displaystyle{ X = \sum_{i=1}^N X_i }[/math], show that [math]\displaystyle{ \textbf{Var}[X] = \textbf{Var}[X_1] \mathbb{E}[N] + \mathbb{E}[X_1]^2 \textbf{Var}[N] }[/math].
• [Moments (I)] Find an example of a random variable with finite [math]\displaystyle{ j }[/math]-th moments for [math]\displaystyle{ 1 \leq j \leq k }[/math] but an unbounded [math]\displaystyle{ (k + 1) }[/math]-th moment. Give a clear argument showing that your choice has these properties.
• [Moments (II)] Let [math]\displaystyle{ X\sim \text{Geo}(p) }[/math] for some [math]\displaystyle{ p \in (0,1) }[/math]. Find [math]\displaystyle{ \mathbb{E}[X^3] }[/math] and [math]\displaystyle{ \mathbb{E}[X^4] }[/math].
• [Moments (III)] Let [math]\displaystyle{ X\sim \text{Pois}(\lambda) }[/math] for some [math]\displaystyle{ \lambda \gt 0 }[/math]. Find [math]\displaystyle{ \mathbb{E}[X^3] }[/math] and [math]\displaystyle{ \mathbb{E}[X^4] }[/math].
• [Covariance and correlation (I)] Let [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] be discrete random variables with correlation [math]\displaystyle{ \rho }[/math]. Show that [math]\displaystyle{ |\rho|\leq 1 }[/math].
• [Covariance and correlation (II)] Let [math]X[/math] and [math]Y[/math] be discrete random variables with mean [math]\displaystyle{ 0 }[/math], variance [math]\displaystyle{ 1 }[/math], and correlation [math]\rho[/math]. Show that [math]\mathbb{E}(\max\{X^2,Y^2\})\leq 1+\sqrt{1-\rho^2}[/math]. (Hint: use the identity [math]\max\{a,b\} = \frac{1}{2}(a+b+|a-b|)[/math].)
• [Covariance and correlation (III)] Let [math]X[/math] and [math]Y[/math] be independent Bernoulli random variables with parameter [math]1/2[/math]. Show that [math]X+Y[/math] and [math]|X-Y|[/math] are dependent though uncorrelated.

Problem 2 (Inequalities)

• [Reverse Markov's inequality] Let [math]\displaystyle{ X }[/math] be a discrete random variable with bounded range [math]\displaystyle{ 0 \le X \le U }[/math] for some [math]\displaystyle{ U \gt 0 }[/math]. Show that [math]\displaystyle{ \mathbf{Pr}(X \le a) \le \frac{U-\mathbf{E}[X]}{U-a} }[/math] for any [math]\displaystyle{ 0 \lt a \lt U }[/math].
• [Markov's inequality] Let [math]\displaystyle{ X }[/math] be a discrete random variable. Show that for all [math]\displaystyle{ \beta \geq 0 }[/math] and all [math]\displaystyle{ x \gt 0 }[/math], [math]\displaystyle{ \mathbf{Pr}(X\geq x)\leq \mathbb{E}(e^{\beta X})e^{-\beta x} }[/math].
• [Cantelli's inequality] Let [math]\displaystyle{ X }[/math] be a discrete random variable with mean [math]\displaystyle{ 0 }[/math] and variance [math]\displaystyle{ \sigma^2 }[/math]. Prove that for any [math]\displaystyle{ \lambda \gt 0 }[/math], [math]\displaystyle{ \mathbf{Pr}[X \ge \lambda] \le \frac{\sigma^2}{\lambda^2+\sigma^2} }[/math]. (Hint: You may first show that [math]\displaystyle{ \mathbf{Pr}[X \ge \lambda] \le \frac{\sigma^2 + u^2}{(\lambda + u)^2} }[/math] for all [math]\displaystyle{ u \gt 0 }[/math].)
• [Chebyshev's inequality (I)] Fix [math]\displaystyle{ 0 \lt b \le a }[/math]. Construct a random variable [math]\displaystyle{ X }[/math] with [math]\displaystyle{ \mathbf{E}[X^2] = b^2 }[/math] for which [math]\displaystyle{ \mathbf{Pr}(|X| \ge a) = b^2/a^2 }[/math].
• [Chebyshev's inequality (II)] Let [math]\displaystyle{ X }[/math] be a random variable with [math]\displaystyle{ 0 \lt \mathbf{E}[X^2] \lt \infty }[/math]. Show that [math]\displaystyle{ \lim_{a \to \infty} \frac{a^2 \mathbf{Pr}(|X| \ge a)}{ \mathbf{E}[X^2] } = 0 }[/math]. (Hint: Use the dominated convergence theorem. For discrete random variables, it can be formualated as follows: Let [math]\displaystyle{ Z,X, X_1,X_2,\ldots,X_n,\ldots }[/math] be discrete random variables with finite second moments. If [math]\displaystyle{ |X_n| \le Z }[/math] and for any [math]\displaystyle{ a \in \mathbb{R} }[/math], [math]\displaystyle{ \mathbf{Pr}(X_n = a) \to \mathbf{Pr}(X = a) }[/math] when [math]\displaystyle{ n }[/math] tends to infinity, then [math]\displaystyle{ \mathbf{E}[X_n^2] \to \mathbf{E}[X^2] }[/math].)

Problem 3 (Probability meets graph theory)

• [Common neighbor] Let [math]\displaystyle{ p \in (0,1) }[/math] be a constant. Show that with a probability approaching to [math]\displaystyle{ 1 }[/math] (as [math]\displaystyle{ n }[/math] tends to infinity) the Erdős–Rényi random graph [math]\displaystyle{ \mathbf{G}(n,p) }[/math] has the property that every pair of its vertices has a common neighbor. (Hint: You may use Markov's inequality.)
• [Isolated vertices] Prove that [math]\displaystyle{ p = \log n/n }[/math] is the threshold probability for the disappearance of isolated vertices. Formally, you are required to show that
  1. with a probability approaching to [math]\displaystyle{ 1 }[/math] (as [math]\displaystyle{ n }[/math] tends to infinity) the Erdős–Rényi random graph [math]\displaystyle{ \mathbf{G} = \mathbf{G}(n,p) }[/math] has the property that [math]\displaystyle{ \mathbf{G} }[/math] has no isolated vertices when [math]\displaystyle{ p = \omega(\log n/n) }[/math];
  2. with a probability approaching to [math]\displaystyle{ 0 }[/math] (as [math]\displaystyle{ n }[/math] tends to infinity) the Erdős–Rényi random graph [math]\displaystyle{ \mathbf{G} = \mathbf{G}(n,p) }[/math] has the property that [math]\displaystyle{ \mathbf{G} }[/math] has no isolated vertices when [math]\displaystyle{ p = o(\log n/n) }[/math].