Table 2.

Considered approximate probabilistic model checking approaches

	Frequentist	Bayesian
Estimate	Chernoff-Hoeffding
	bounds [49]	Mean and variance [50]
Hypothesis testing	Statistical [51]	Statistical [17]
	Probabilistic
	black-box [52,53]

Bayesian methods consider prior knowledge about the parameters and variables in the model when deciding if a logic property holds. Conversely frequentist approaches assume no prior knowledge is available. All methods except probabilistic black-box take as input a user-defined upper bound on the approximation error. They request additional model executions until the result is sufficiently accurate. Probabilistic black-box model checking takes a fixed number of model simulations as input and computes a p-value as the confidence measure of the result.