Next, a set of samples (i.e., the sample matrix) are generated from the joint probability distribution of all selected input variables to represent the entire problem space. When all input variables are independent of each other, generating samples from their joint probability distribution is equivalent to sampling separately from the probability distribution of each individual model input. Otherwise, correlations between input variables need to be characterized to properly define and generate samples from their joint probability distribution. Different techniques can be used for sample generation, and the selection of technique is often determined by the types of uncertainty and sensitivity analyses of interest (Section 5.3). In practice, Monte Carlo sampling (i.e., random sampling) generates samples by repeatedly drawing random values from a defined distribution using random number generators. Alternatively, low-discrepancy, quasi-random sequences (e.g., Latin hypercube sampling, Ideally, aleatory uncertainty and epistemic uncertainty should be propagated through the model separately. However, such separation is costly and not always possible. Nonetheless, this distinction should be reflected in the interpretation of the uncertainty and sensitivity analysis results. For example, when the prediction uncertainty of system performance is largely attributed to a lack of knowledge of the true value of a technological parameter, research should focus on reducing the epistemic uncertainty of this parameter (e.g., through more accurate experimental measurement to narrow the possible range of its true value). On the other hand, if decision variables are found to be the driver of the variation in the predicted system performance, development of the technology should focus on optimizing the design and operation decisions to limit the aleatory uncertainty.