In contrast to deterministic models, what do probabilistic models do?

Probabilistic models are designed to handle uncertainty and variability in data and outcomes. Unlike deterministic models, which always produce the same output given a specific set of inputs, probabilistic models incorporate randomness and provide outcomes that vary according to certain probabilities.

This means that when you run a probabilistic model, you may receive different results on different occasions, even with the same initial conditions. This variability is a reflection of the inherent uncertainty in real-world situations, where many factors can influence outcomes.

In various applications, such as in natural language processing, predictive analytics, or risk assessment, probabilistic models allow for a more nuanced and realistic representation of the complexities involved, making them essential for tasks where uncertainty plays a significant role.