| | 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.
