In a Bayesian framework, replication is pretty easy to make predictions on.

Suppose there is a parameter - say the probability q of a Bernoulli random variable being true. Based on your past experiments you have a posterior p(q). Then given p(q), you can easily compute a probability distribution on S(N), where S(N) is the number of successes you'd get from a bernoulli random variable with N attempts.

In code terms, you are just computing posterior.flatMap(q => Bernoulli(N,q)). (Using the inherent monadic structure of probability.)

This actually works in general. If you want to predict the outcome of a later experiment (the replication) you just compute posterior.flatMap(parameter => generateResult(parameter)).