Geometric Distribution

Geometric Distribution#

The probability of the first success given by \(p\) to be at position \(n\).

\[\begin{split} \begin{gather*} X \sim \text{Geometric}(p)\\ P (X = n) = q^{n-1}p,\quad \qquad p+q=1 \end{gather*} \end{split}\]

Expectation : $\( E[X] = \frac{1}{p} \)$

::: Proof
The expected value of first success after $W$ can be done quickly by noticing

* There must be at least one trial.
* If the first trial succeeds (with probability $p$), $W=1$.
* If the first trial fails (with probability $q$), the remaining trials is also distributed equallty to $W$.


Let $W = 1 + R$ where $R$ is the random variable for the number of trials after the first. Let $W'$ be distributed equallty to $W$.

$$
W = 1 + R\\
$$

$$
\begin{align}
    \mathbb E[W] &= 1 + \mathbb E[R]\\
    &= 1 + q\mathbb E[W']\\
    &= 1 + q\mathbb E[W]\\
\end{align}
$$

$$
\mathbb E[W] = \frac{1}{q}
$$
:::

Variance : $\(E[X^2] = \frac{q}{p^2} \)$

::: Proof
We can prove this using conditional variance. Notice that $X$ can be expressed as a mixture of two distributions: a constant with chance $p$, and IID $1 + X^*$ with chance $q$

$$
\begin{gather*}
P(X=1) = p \\
P(X = 1 + X^*) = q
\end{gather*}
$$

The variance by conditioning is then,

$$
\begin{align*}
\text{Var}(X) &= [\text{Var}(1) + E(1)^2]p + [\text{Var}(1+X^*) + E(1 + X^*)^2]q - E(X)^2\\
&= p + \left[\text{Var(X)} + \frac{1}{p^2} + 1 \right]q - \frac{1}{p^2}\\
&= \frac{1-p}{p^2}\\
&= \frac{q}{p^2}
\end{align*}
$$
:::

Coupon Collector’s Problem#

Let’s say there are \(n\) types of coupons and you need all \(n\) to win a prize. To get a coupon you must buy some product.

  1. What is the expected number of buys to get all \(n\) coupons given uniform probability of getting any coupon.

    Let \(X_i\) be the random variable of the number of buys to get a unique coupon. You need \(n\) unique coupons so the number of buys to win a prize is the sum of \(X_i\).

    \[\begin{split} \mathbb E[X_i] = \frac{1}{p_i} = \frac{n}{n-(i-1)}\\ \mathbb E\left [\sum{X_i} \right] = n \sum_{k=1}^n \frac{1}{k} \end{split}\]
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