I need help trying to understand how values can be used in a beta distribution to represent priors/posterior probabilities. I'm aware that there are similar questions that have already been asked about beta distribution, but they don't really ask what I'm having trouble with.
In the following example we have:
p = a prior
D: Some data, with 16/20 success
and two parameters , (1.1,1.3).
I know that using R we can plot the data:
p <- seq(from = 0, to = 1 , len = 10) plot(p,dbeta(p,1.1,1.3)
What I'm having trouble understanding is how the parameters relate to the rest of the information?
Do the parameters represent the prior probabilities, and if so, in order to find the posterior probability, could i just use the Bayes theorem?
Would I need to make an opinionated guess for the priors in order to find the posterior probabilities, or is that what the two parameters represent in this case?