What is a key characteristic of a Markov model?

A key characteristic of a Markov model is that the probabilities depend only on the current state, not on the sequence of events that preceded it. This property is known as the Markov property, which means that the future state of a system is conditionally independent of its past given the present state. Thus, the next state depends solely on the current state and not on how that state was reached.

This characteristic makes Markov models particularly useful for modeling a variety of stochastic processes, including those found in AI applications such as speech recognition, natural language processing, and decision-making systems. It simplifies the modeling process, allowing for a more straightforward analysis and implementation.

In contrast, other concepts like deterministic models involve predictable outcomes based solely on current inputs, while the notion of modeling random processes directly contradicts the premise of the Markov model's reliance on uncertainty and probabilistic transitions.