The probabilistic model makes it possible to define the **probability distributions** for each of your parameters. A large number of distributions are available, both **continuous and discrete**. You may also define the **correlation** between your variables thanks to copulas.

Defining the probabilistic model unlocks some features in Persalys such as:

- Sensitivity analysis
- Central tendency analysis
- Reliability analysis

For more information on how to create a probabilistic model, refer to the following video (soon in English):