What technique utilizes random sampling to evaluate the probabilistic properties of a model or system?

The technique that employs random sampling to assess the probabilistic characteristics of a model or system is Monte Carlo simulation. This method is powerful for understanding the impact of risk and uncertainty in forecasting and decision-making. By generating a large number of simulated scenarios, Monte Carlo simulation allows analysts to evaluate a range of possible outcomes and the likelihood of each, effectively capturing the variability and complexity inherent in many real-world systems.

In contrast, scenario analysis typically evaluates specific potential future events based on distinct and predefined situations rather than sampling based on probability distributions. Regression analysis focuses on understanding relationships between dependent and independent variables, providing estimates but not the broad range of variability seen in Monte Carlo simulations. Time series analysis is concerned with analyzing data collected over time to identify trends, cycles, or seasonal variations, rather than assessing uncertainty through random sampling. This distinction makes Monte Carlo simulation the appropriate choice for evaluating probabilistic properties.