What is a Markov model?

A Markov model is best defined as a stochastic model where the probabilities of future events depend solely on the current state, rather than any prior states. This characteristic is referred to as the "Markov property," which captures the idea that the future is independent of the past given the present.

In more practical terms, Markov models can be applied in various fields such as statistics, economics, and machine learning to predict a series of events based on their likelihood. They are particularly useful in scenarios where systems undergo transitions from one state to another, such as weather forecasting, stock price movements, or even in AI for decision-making processes where only the current status matters.

The other options do not accurately represent what a Markov model is. For instance, while historical data may be part of the framework to determine transition probabilities in practice, the core principle of a Markov model is its reliance on the current state alone. Programming methods or task management approaches do not align with the mathematical and probabilistic nature of Markov models. Similarly, while network models can represent various system architectures, they do not specifically relate to the probabilistic behavior and state transitions defined by Markov models.