Paper 2, Section II, 12F

Probability | Part IA, 2021

State and prove Chebyshev's inequality.

Let (Xi)i⩾1\left(X_{i}\right)_{i \geqslant 1} be a sequence of independent, identically distributed random variables such that

P(Xi=0)=p and P(Xi=1)=1−p\mathbb{P}\left(X_{i}=0\right)=p \text { and } \mathbb{P}\left(X_{i}=1\right)=1-p

for some p∈[0,1]p \in[0,1], and let f:[0,1]→Rf:[0,1] \rightarrow \mathbb{R} be a continuous function.

(i) Prove that

Bn(p):=E(f(X1+⋯+Xnn))B_{n}(p):=\mathbb{E}\left(f\left(\frac{X_{1}+\cdots+X_{n}}{n}\right)\right)

is a polynomial function of pp, for any natural number nn.

(ii) Let δ>0\delta>0. Prove that

∑k∈Kδ(nk)pk(1−p)n−k⩽14nδ2\sum_{k \in K_{\delta}}\left(\begin{array}{l} n \\ k \end{array}\right) p^{k}(1-p)^{n-k} \leqslant \frac{1}{4 n \delta^{2}}

where KδK_{\delta} is the set of natural numbers 0⩽k⩽n0 \leqslant k \leqslant n such that ∣k/n−p∣>δ|k / n-p|>\delta.

(iii) Show that

sup⁡p∈[0,1]∣f(p)−Bn(p)∣→0\sup _{p \in[0,1]}\left|f(p)-B_{n}(p)\right| \rightarrow 0

as n→∞n \rightarrow \infty. [You may use without proof that, for any ϵ>0\epsilon>0, there is a δ>0\delta>0 such that ∣f(x)−f(y)∣⩽ϵ|f(x)-f(y)| \leqslant \epsilon for all x,y∈[0,1]x, y \in[0,1] with ∣x−y∣⩽δ|x-y| \leqslant \delta.]

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