I've got a list of $99$ values

$y_{0.01}, y_{0.02}, ..., y_{0.99}$

which are drawn from a distribution ("$y_{0.01}$" is the y-value of the PDF for {x = 0.01}). I don't know the PDF that corresponds to these values; the values are all I've got.

I'm trying to pick a "random" number from the interval $(0,1)$, but subject to the given values. So, for instance, if $y_{0.53}$ is the maximal value in my list, then the probability of my random value being $0.53$ should be the highest compared to all other values in my list. I'm wondering how to best go about this.

I was thinking if I could somehow figure out the quantile function that corresponds to my list, i.e. the inverse of the CDF of the distribution, then I could simply pick a random number on $(0,1)$ and plug that number into this function. But I only have the values on my list...

  • $\begingroup$ have you tried fitting this distribution to a normal or lognormal or ... parametric distribution? $\endgroup$ – phdmba7of12 Aug 9 '19 at 15:24
  • $\begingroup$ The initial values are based on a beta distribution, but they're subject to change over time... $\endgroup$ – sdlkgjh45 Aug 9 '19 at 15:25

To clarify your question: you have 99 samples of PDF $f_X(x)$ at points $x = 0.01, 0.02, ... 0.99$. You want to pick a number from the distribution that has overall unknown PDF $f_X(x)$.

The first step is to convert the PDF to a PMF of corresponding discrete distribution. Let the discrete RV is $W$. Let PMF of $W$ is $P_W(w)$.

Since, you have the PDF values at $x = 0.5$ (for example), $P(0.495 < x < 0.505) = y_{0.50} \times (0.505 - 0.495)$. For discrete case, it is equivalent to $P_W(w = 0.5) = y_{0.5} \times 0.01$.

With this PMF, you can calculate the CDF, make a table (or write a general function), and do a reverse lookup to get inverse CDF function. Then, as you mentioned, a uniform random variable between (0,1) should give you a number with given PMF $P_W(w)$.

Note that this method gives you the precision of 0.01. If you want more precision, you can interpolate the sequence $y_i$. One way is to fit a curve or create a step function that passes through $(i, y_i)$ and consider the new function as "finely" sampled PDF $f_X(x)$.



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