Question

Why do we generally prefer a probabilistic model to a deterministic model? Give examples for when the two types of models might be appropriate.

Short answer

A deterministic model offers a single possible outcome for an event, whereas a probabilistic model offers a probability distribution as a solution.

Step-by-step solution

Introduction.

The outcome of a deterministic model is completely defined by the parameter settings and initial values, whereas probabilistic (or random) models integrate randomness into their approach.

Determine why do we choose probabilistic models over deterministic models?

A random element is included in a probabilistic model. Even with the identical beginning circumstances, the model is likely to provide different outcomes each time it is run. A probabilistic model involves some element of random variation. Probabilistic model When we cannot predict y exactly, we utilize a probabilistic model (there will be errors). Assume we wish to give a student a mark on a test depending on the number of hours he studied for the exam.

A deterministic model contains no random components. You will obtain the same results every time you run the model with the identical beginning circumstances. Deterministic model when we can exactly predict y, we employ a deterministic model (with no errors). Assume we wish to anticipate ticket sales at a performance based on the number of tickets sold.

Therefore, this is why probabilistic model is preferred over deterministic model.