## Sunday, September 18, 2011

### Probability interlude: from discrete events to continuous time

Lunchtime fun: the relationship between Bernoulli and Exponential distributions.

Let's say the probability of Joe getting a coupon for Pepsi in any given time interval $\Delta t$, say a month, is given by $p$. This probability depends on a number of things, such as intensity of couponing activity, quality of targeting, Joe not throwing away all junk mail, etc.

For a given integer number of months, $n$, we can easily compute the probability, $P$, of Joe getting at least one coupon during the period, which we'll call $t$, as

$P(n) = 1 - (1-p)^n$.

Since the period $t$  is $t= n \times \Delta t$, we can write that as

$P(t) = 1 - (1-p)^{\frac{t}{\Delta t}}.$

Or, with a bunch of assumptions that we'll assume away,

$P(t) = 1- \exp\left(t \times \frac{\log (1-p)}{\Delta t}\right).$

Note that $\log (1-p)<0$. Defining $r = - \log (1-p) /\Delta t$, we get

$P(t) = 1 - \exp (- r t)$.

And that is the relationship between the Bernoulli distribution and the Exponential distribution.

We can now build continuous-time analyses of couponing activity. Continuous analysis is much easier to do than discrete analysis. Also, though most simulators are, by computational necessity, discrete, building them based on continuous time models is usually simpler and easier to explain to managers using them.