A probabilistic model is a mathematical framework used to represent uncertain situations by incorporating randomness. It helps predict outcomes by assigning probabilities to different events or scenarios. These models are widely used in various fields, including statistics, machine learning, and finance, to make informed decisions based on incomplete information. In a probabilistic model, the relationships between variables are defined using probability distributions. For example, in a Bayesian network, nodes represent variables, and edges indicate dependencies, allowing for the calculation of the likelihood of various outcomes. This approach enables better understanding and management of uncertainty in complex systems.