In a first-order Markov model, what does the next state depend on?

In a first-order Markov model, the defining characteristic is that the next state depends solely on the current state. This property is known as the Markov property, which asserts that given the present state, all past states become irrelevant for predicting future states. Essentially, this means the transition into the next state does not require knowledge of how the current state was reached; it relies only on the information encapsulated in the current state itself.

This model effectively simplifies the complexity of predicting future states by reducing the dependencies to just the most recent state, making it computationally efficient and easy to manage in various applications such as natural language processing and systems modeling.