I've recently run into a situation where I know a few probability points in the tail of a distribution and I want to "fit" a distribution that goes through these points in the tail. I realize this is messy and not overly accurate, and plagued with conceptual issues. However, trust me that I really want to do this.

So effectively I know some points in the tail of the CDF with x being the values and y being the probability of that value or smaller. Here's R code to illustrate my data:

x <- c(0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85)
y <- c(0.0666666666666667, 0.0625, 0.0659340659340659, 0.0563106796116505, 
       0.0305676855895196, 0.0436953807740325, 0.0267459138187221)

Then I create a function to minimize error between my data and a beta distribution CDF using pbeta. I use SSE as a fit metric then minimize that with -sum. I throw in an initial guess as the first param to optim of (9, .8) although I've tried this with different guesses and I always get the same result. The starting point guess I use comes from manually cooking up parameters by hand that seem close.

# function to optomize with optim
beta_func <- function(par, x) -sum( (pbeta( x, par[1], par[2]) - y)**2 ) 
out <- optim(c(9,.8), beta_func, lower=c(1,.5), upper=c(200,200), method="L-BFGS-B", x=x)

out <- out$par
#> [1]  0.90000 23.40294

Below I graph the 'optimized' beta distribution in red, my actual data in blue, and a hand tweaked starting guess of the beta parameters in black.

plot(function(x) pbeta(x, shape1=out[1], shape2=out[2] ), 0, 1.5, col='red')
plot(function(x) pbeta(x, 9,.8), 0, 1.5, col='black', add=TRUE)
lines(x,y, col='blue')

enter image description here

I can't grok what's going on with optim to give a solution that's worse than my starting guess. I calculated the SSE for my starting guess vs the optim solution and it looks like my guess has a much larger -SSE:

# my guess
-sum( (pbeta( x, 9, .8) - y)**2)
#> [1] -0.03493344

# optim's output
-sum( (pbeta( x, .9, 23) - y)**2)
#> [1] -6.314587

Using past history as my Bayesian prior, my guess is that I'm misunderstanding optim or feeding it improper inputs. However, I can't grok what's going on. Any tips would be greatly appreciated.

I've tried using the CG optimization method, but the results are not meaningfully different and still don't seem near as good as my starting guess.

out <- optim(c(9,.8), beta_func, method="CG", x=x)
out <- out$par
#> [1]  2.287611 11.124736
  • 1
    $\begingroup$ How can these points be "known" for a CDF and yet tend to decrease with increasing values of $x$ ? $\endgroup$
    – JimB
    Commented May 19, 2018 at 15:30
  • 1
    $\begingroup$ I think these are problems which would disappear if you were to estimate these quantities using standard statistical tools instead of re-inventing the wheel. $\endgroup$
    – Sycorax
    Commented May 19, 2018 at 16:02
  • $\begingroup$ @JimB the source of the downward slope is two fold. Estimates of each point come from a different sampling process so two things happen: 1) sample noise 2) violation of the iid assumption about each underlying. I'm not concerned as much with the stats issues here as much as trying to make optim do my bidding. $\endgroup$
    – JD Long
    Commented May 19, 2018 at 17:53
  • $\begingroup$ @Sycorax your suggestion presumes that standard statistical tools would work in my situation. That's an incorrect assumption. $\endgroup$
    – JD Long
    Commented May 19, 2018 at 17:55

1 Answer 1


I think you are accidentally trying to maximize the squared errors. The default for optim() is to minimize the function, so the negative sign in your beta_func() results in searching for a max.

If you modify your function like so you get values closer to your guess:

beta_func2 <- function(par, x) sum( (pbeta( x, par[1], par[2]) - y)**2 ) 
out2 <- optim(c(9,.8), beta_func2, lower=c(1,.5), upper=c(200,200), method="L-BFGS-B", x=x)
out2 <- out2$par
[1] 11.04296  0.50000

You can check the new function against your observations (where out, x, and y are defined as in your example):

plot(x,(pbeta(x,out[1],out[2])), ylim=c(-.1,1), col="red", type="l")
points(x, (pbeta(x,9,0.8)), col="black", type="l")
points(x,(pbeta(x,out2[1],out2[2])), col="orange", type="l")
lines(x,y, col='blue')
title(main="Blue observed CDF, black guesstimate, gold optimized")

enter image description here

  • 1
    $\begingroup$ yep, that was clearly my mistake. I thought I had tried changing the sign, on a whim, but clearly I had not. I was pretty anchored to the idea the the default behavior of optim was to maximize. Thanks! $\endgroup$
    – JD Long
    Commented May 19, 2018 at 17:50

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