| Algorithm | Description |
| SUFI-2 | Uncertainty in parameters are expressed as ranges (uniform distributions). The algorithm accounts for all sources of uncertainties (driving variables, conceptual model, parameters and measured data. |
| GLUE | After defining the generalized likelihood measure, a large number of parameter sets are randomly sampled from the prior distribution and each parameter set is assessed as either behavioral or non-behavioral through a comparison of the likelihood measure with the given threshold value. Then, each behavioral parameter is given a likelihood weight. Finally, the uncertainty is predicted. |
| PSO | This algorithm represents a population based stochastic optimization technique. It is initialized with a group of random particles (solutions) and then searches for optima by updating generations. |
| PARASOL | The PARASOL method uses objective functions (OF) into a global optimization criterion (GOC), minimizes these OF or GOC using the Shuffle Complex (SCE-UA) algorithm and performs uncertainty analysis. |
| MCMC | MCMC generates samples from a random walk which adapts to the posterior distribution |