We will prove Weierstrass’ approximation theorem by coin flips

Bernstein polynomials

Let \(f_n:D \to \mathbb{R}\) be a sequence of functions. Recall that \(f_n \to f\) uniformly in \(D\) if \[\lim_{n \to \infty}\sup_{x \in D} |f_n(x) - f(x)| =0.\]

Theorem (Weierstrass):

Let \(f:[0,1] \to \mathbb{R}\) be a continuous function. There exists a sequence of polynomials \(f_n\) that converge uniformly to \(f\).

Exercise: Show that Weierstrass’ theorem holds on any bounded interval \([a,b]\).

The proof we present here, uses probability is due to Bernstein.

Proof (Bernstein): Let \(f:[0,1] \to \mathbb{R}\) be continuous. Let \(p\in (0,1)\). Let \(S_n \sim Bern(n,p)\). Consider the Bernstein polynomial given by \[f_n(p) = \mathbb{E}_p(f(S_n/n)) = \sum_{k=0} ^n {n \choose k}f(k/n)p^k (1-p)^{n-k};\] notice that \(f_n(1)= f(1)\) and \(f_n(0) = f(0)\).

Let \(\epsilon>0\). Note that since \(f\) is continuous on the compact interval \([0,1]\), it is uniformly continuous on \([0,1]\); so that there is a \(\delta >0\) (which depends only on \(\epsilon\) and is independent of \(x\) and \(y\)) such that if \(|x - y| < \delta\), then \(|f(x) - f(y)| < \epsilon\). Also, let \(M= \sup_{x \in [0,1]} |f(x)|\). Thus for all \(p \in [0,1]\), we have that

\[ \begin{eqnarray*} |f_n(p) - f(p)| & = & | \mathbb{E}_p f(S_n/n) - \mathbb{E}_p f(p) | \\ &\leq & \mathbb{E}_p | f(S_n/n) - f(p)| \\ &\leq& \epsilon + 2M\mathbb{P}_p( |S_n/n - p| > \delta) \\ &\leq& \epsilon + 2M\delta^{-2} \mathbb{E}_p|S_n/n -p|^2 \\ &=& \epsilon + 2M \delta^{-2} \mathrm{var}_p(S_n/n) \\ &=& \epsilon + 2M \delta^{-2} p(1-p)/n \\ &\leq& \epsilon + M\delta^{-2} /2n \end{eqnarray*} \] Thus \(\limsup_{n \to \infty} \sup_{p \in [0,1]} |f_n(p) - f(p)| \leq \epsilon\).

Exercise: Let \(f:[0,1] \to \mathbb{R}\) be a continuous function. Assume that for all nonnegative integers \(n\) we have,
\[ \int_0 ^1 x^n f(x)dx = 0\] Show that \(f=0\). Hint: you may want to use the fact that if \(\int_0^1 |f(x)|^2 dx = 0\), then \(f =0\).

Exercise: Let \(X\) and \(Y\) be continuous random variables with continuous pdfs that are supported in \([0,1]\). Show that if \(\mathbb{E} X^n = \mathbb{E} Y^n\) for all nonnegative intergers \(n\), then \(X\) and \(Y\) have the same law.