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A deterministic algorithm is an algorithm that is purely determined by its inputs, where no randomness is involved in the model. Deterministic algorithms will always come up with the same result given the same inputs.
By contrast, probabilistic models include an element of probability. One way to think about probabilistic and deterministic models is to think about linear programming, where in earlier traditional paradigms, results were purely deterministic.
More recently, with the development of weighted inputs and various tools, programs can inject an element of probability into results which often provide sophisticated dynamic results instead of static results that are associated with purely deterministic algorithms.
If, for example, a machine learning program takes a certain set of inputs and chooses one of a set of array units based on probability, that action may have to be “verified” by a deterministic model – or the machine will continue to make these choices and self-analyze to “learn” in the conceptual sense.