What type of model utilizes sampling to predict the next piece of data?

The probabilistic model is designed to use sampling techniques to forecast or predict future data points based on observed patterns and distributions in the available data. This model accounts for uncertainty and variability, allowing it to make predictions that reflect possible outcomes rather than certainties.

In scenarios where there's inherent randomness or uncertainty in the data, probabilistic models leverage statistical techniques to estimate the likelihood of various outcomes. For instance, when predicting a customer's purchase behavior, a probabilistic model might analyze past customer interactions to estimate the chances of a new customer making similar purchases. This approach is particularly useful in environments where data is noisy or where less is known, allowing for a nuanced prediction method that can adapt as more data is collected.

On the other hand, deterministic models provide a specific output based on given inputs without accounting for variability or randomness. Linear regression models, while useful for predicting outcomes based on relationships between variables, do not inherently involve sampling in the same way probabilistic models do. Hierarchical models focus on structured data but also do not directly encompass the sampling aspect involved in making predictions about the next piece of data. Hence, the choice of a probabilistic model fits the requirement of utilizing sampling effectively to predict future events or data points.