What distributions could help describe my uncertainty about a probabilistic forecast? I'm dealing with binary events and I've got people guestimating the chance that they occur.  I'd like to translate someone else's guestimate into a probability distribution representing my belief about the true probability.  This is called a posterior distribution right?  basic bayesian stats?
I imagine I want a distribution which looks roughly normal and fairly wide for a forecast of 0.5 but becomes increasingly skewed for forecasts closer to the tails.
At the moment I don't have data, I'm just doing exploratory simulations.  I'm looking for an R function to that takes a probability forecast and outputs a probability distribution with reasonable parameters.
Added:  For my purposes, even a very very rough approximation will do.  Its perfectly fine if statistically literate people roll their eyes.  But R code to get me started would be greatly appreciated.
Added:  I fear I still wasn't clear enough about my weird situation.  My "expert" is actually only telling me their single best guess for the binary event occurring.  I've seen (but not recorded) a bunch of the expert's past best guesses so I have an intuitive sense for what the true distribution of the probability of the events are conditional on an expert's best guess.  So I guess I need to play around with beta distribution parameters.  Find the parameters that I like for a 0.5 forecast, a 0.25 forecast, 0.1, 0.05 and 0, and then find some smooth functions to interpolate my guesses at what the parameters alpha and beta should be for the space between?
 A: To expand on @Glen_b's answer.  A beta distribution is the standard answer here, although it should be noted that it is possible (and also not uncommon) to not try to smooth/model the prior distribution at all but just use some raw point estimates... putting that aside, let us say you think the probability of an occurance can be anywhere between 0 and 1 but you have an expert guess of the chance of that probability being correct for each of 0.1, 0.2, etc.  That is, your expert (perhaps you) thinks the chance that that probability is 0.1 is only 0.01; the chance it really is 0.2 is 0.015, and so on.
You can use the method of moments as neatly set out for you in the Wikipedia article on the beta distribution.  Here is some code to get you started:
expert <- data.frame(
    probs=1:9/10, 
    prior=c(.01,.15,.3,.2,.14, .1,.05,.04,.01)
)

sum(expert$prior) # check equal to 1

That is, the prior estimates of the chance of each probability are:
> expert
  probs prior
1   0.1  0.01
2   0.2  0.15
3   0.3  0.30
4   0.4  0.20
5   0.5  0.14
6   0.6  0.10
7   0.7  0.05
8   0.8  0.04
9   0.9  0.01

So now to use the method of moments to turn this into a beta distribution that can summarise it:
u <- with(expert, probs %*% prior)
v <- with(expert, ((probs-u)^2) %*% prior)

alpha <- u* (  (u*(1-u)) /v  -1)
beta <- (1-u) * (( u*(1-u))/v-1)

x <- 0:1000/1000

png("beta and point estimates.png", 400,400, res=72)
    plot(x, dbeta(x, alpha, beta), type="l", bty="l", ylim=c(0,3), 
        ylab="Density", xlab="Probability",
        main="Original expert point estimates,\nand beta distribution")
    with(expert, points(probs, prior*10))
dev.off()

You can see from the image below that the beta distribution is an ok summary of the expert's opinion but it isn't quite the same - basically, the expert's view on the chance of 0.3 being the true probability are a bit of a spike in their prior distribution.

From here you can use Bayesian techniques to confront your prior distribution with the likelihood of the actual data, and hence produce a posterior distribution that combines the two.
A: I suspect something like a beta distribution might serve your needs. It's often used to model probabilities.
