Thanks for the comment.
@Max
After I asked the question, I came up with my own code to make it happen.
The following code was created to get the posterior distribution of performance for logistic.
The goal was to get the posterior distribution of the logistic of perf_mod.
The upper part of the figure below.

library(rstan)
resample_df <- structure(list(id = c("Fold01", "Fold02", "Fold03", "Fold04",
"Fold05", "Fold06", "Fold07", "Fold08", "Fold09", "Fold10"),
logistic = c(0.855873015873016, 0.933116883116883, 0.933793103448276,
0.86436170212766, 0.847402597402597, 0.911424903722721, 0.867137355584082,
0.88563829787234, 0.897946084724005, 0.906330749354005),
mars = c(0.845079365079365, 0.951298701298701, 0.937241379310345,
0.858377659574468, 0.853896103896104, 0.839537869062901,
0.858472400513479, 0.875664893617021, 0.897946084724005,
0.893733850129199)), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame"))
stan_code <- "
data {
int length;
real x[length];
}
parameters {
real mu;
real<lower=0> sigma;
}
model {
x ~ normal(mu, sigma);
}
"
data <- list(x = resample_df$logistic,
length = nrow(resample_df))
mod = rstan::stan_model(model_code = stan_code)
fit = rstan::sampling(mod,
data = data,
iter = 10000,
chains = 3)
stan_dens(fit)
Doesn't this mean that the posterior distribution of the model's performance was obtained from 10 data points?