# Probability Notes

By convention:

$$E^n[X] \stackrel{def}{=} (E[X])^n$$

## Independence of Expectation (finite)

Claim:

$$E[ \sum_{k=0}^{n-1} X_k ] = \sum_{k=0}^{n-1} E[X_k]$$

Proof:

\begin{align} E[ X + Y ] & = \int \int (s + t) \Pr\{ X = s \ \& \ Y = t \} \ ds \ dt \\ & = \int \int s \Pr \{ X = s \ \& \ Y = t \} \ ds \ dt + \int \int t \Pr \{ X = s \ \& \ Y = t \} \ ds \ dt \\ & = \int \int s \Pr \{ X = s \ \& \ Y = t \} \ dt \ ds + \int \int t \Pr \{ X = s \ \& \ Y = t \} \ ds \ dt \\ & = \int s \Pr \{ X = s \} \ ds + \int t \Pr \{ Y = t \} \ dt \\ & = E[X] + E[Y] \end{align}

Induction can be used to extend to the general case:

$$E[ \sum_{k=0}^{n-1} X_k ] = \sum_{k=0}^{n-1} E[X_k]$$

## Bayes' Theorem

$$\Pr\{ A | B \} = \frac{ \Pr\{ A \& B \} }{ \Pr\{ B \} }$$

$$\Pr\{ B | A \} = \frac{ \Pr\{ A \& B \} }{ \Pr\{ A \} }$$

$$\Pr\{ A | B \} = \frac{ \Pr\{ B | A \} \Pr\{ A \} }{ \Pr\{ B \} }$$

## Variance

$$\mathrm{Var}[X] \stackrel{def}{=} E[(X - E[X])^2] = E[X^2] - (E[X])^2$$

## Moment Generating Functions

$$M_X(t) \stackrel{def}{=} E[ e^{t X} ] = \sum_{k=0}^{\infty} \frac{t^k E[X^k]}{k!}$$

If $X$ and $Y$ and independent random variables, then:

$$M_{X + Y}(t) = E[ e^{t(X + Y)} ] = E[ e^{tX} e^{tY} ] = M_X(t) \cdot M_Y(t)$$

\begin{align} \frac{d^n}{dt} M_X(t) & = \frac{d^{(n)}}{dt} ( \sum_{k=0}^{\infty} \frac{t^n E[X^n]}{k!} ) \\ & = \sum_{k=n} \frac{t^{k-n} E[X^k]}{(k-n)!} \\ \to \frac{d^n}{dt} M_X(0) & = E[X^n] \end{align}

## Jensen's Inequality

Claim:

If $f(x)$ is a convex function, then:

$$E[f(X)] \ge f(E[X])$$

Proof:

Taylor's theorem gives us:

$$\exists\ c : f(x) = f(\mu) + f'(\mu)(x - \mu) + \frac{f''(c)(x-\mu)^2}{2}$$

Since $f(x)$ is concave, we know:

$$f(\mu) + f'(\mu)(x - \mu) + \frac{f''(c)(x-\mu)^2}{2} \ge f(\mu) + f'(\mu)(x-\mu)$$

This gives us:

$$E[f(X)] \ge E[ f(\mu) + f'(\mu)(X - \mu) ]$$

Choose $\mu = E[X]$:

\begin{align} E[ f(\mu) + f'(\mu)(X-\mu) ] & = E[ f(E[X]) + f'(E[X])(X - E[X]) ] \\ & = E[ f( E[X] ) ] + f'(E[X])(E[X] - E[E[X]]) \\ & = f(E[X]) + 0 \\ \end{align}

$$\to E[f(X)] \ge f(E[X])$$

## Markov's Inequality

Claim:

$$X \ge 0, a > 0$$

$$\Pr \{ X \ge a \} \le \frac{E[X]}{a}$$

Proof:

Since $X \ge 0$ and $a > 0$:

\begin{align} E[X] & = \int_0^{\infty} t\ p_X(t) dt \\ & = \int_0^{a} t\ p_X(t) dt + \int_a^{\infty} t\ p_X(t) dt \\ & \ge \int_{a}^{\infty} t\ p_X(t) dt \\ & \ge \int_{a}^{\infty} a\ p_X(t) dt \\ & = a \int_{a}^{\infty} p_X(t) dt \\ & = a \Pr\{ X \ge a \} \\ \end{align}

$a > 0$, so we can divide:

$$\to \Pr\{X \ge a \} \le \frac{E[X]}{a}$$

## Chebyshev's Inequality

Claim:

$$a > 0$$

$$\Pr\{|X - E[X]| \ge a \} \le \frac{ \mathrm{Var}[X] }{a^2}$$

Proof:

\begin{align} \Pr\{ |X - E[X]| \ge a \} & = \Pr\{ (X - E[X])^2 \ge a^2 \} \\ & \le \frac{E[ (X-E[X])^2 ]}{a^2} \\ & = \frac{\mathrm{Var}[X]}{a^2} \end{align}

By Markov's and the definition of variance.

## Chernoff Bound

$$X \ge 0, a > 0$$

$$\Pr\{ X \ge a \} = \Pr\{ e^{tX} \ge e^{ta} \} \le \frac{E[e^{tX}]}{e^{ta}}$$

$$\Pr\{ X \ge a \} \le \min_{t>0} \frac{E[e^{tX}]}{e^{ta}}$$

This can be seen by a straight forward application of Markov's inequality. The parameter $t$ can be chosen to taste.