# Prior estimation method from point estimate

In order to use a point estimate as a prior in a beta distribution, you need to convert it to `a`

and `b`

values. (In SkateAnalytics, these are successes or failures.) I figured out that given a point estimate like 0.6, can turn this into a fraction and then solve for `a`

and `b`

using the numerator and denominator: `a = numerator`

, `b = denominator - numerator`

. (I thought of this from the probability formula, which is `a / (a + b)`

.)

0.6 converts to a fraction pretty easily, but many other values don't, such as 0.7981662. Thankfully, there is a way to use programming to convert numbers into fractions and get their numerators and denominators, which we can then plug into a beta distribution. (I have found a way to do this in R.)

However, this can lead to really large values for the sum of `a`

and `b`

(collectively called `N`

) if the original point estimate has more than one one number after the decimal. Since we're using this to establish a prior to then build a posterior distribution, this large N causes problems for us if the point estimate that we've been given isn't as reliable or credible (imagine if it was truly a random number generated between 0 and 1). The large N contributes "a lot" of information, which can swamp our actual data.

Before making the fraction form of the point estimate, we can first round it to the nearest `1 / N`

, with this `N`

being the number of observations that we're OK with contributing to our calculation of the posterior distribution. (I realized that `1 / N`

is the general form that we need after thinking about this problem for a long while as rounding to the nearest 0.1, as I felt that 10 implied observations seems OK and would likely make it easier for the computer to compute the fractional form of the result.)

How large is a "really large" `N`

? I'm not sure, but that's something that I think we can test with simulations.