are there any limitations in markov chains?
Yes, there are several limitations to Markov chains:
1. Markov assumption: Markov chains assume that the future state of a system only depends on its current state, and not on its past history. This assumption may not hold in many real-world scenarios where the future can be influenced by past events.
2. Memory-less property: Markov chains do not consider the duration of time spent in each state or the sequence of transitions. They only focus on the current state and its transition probabilities, making them memory-less. Consequently, they may not capture important information about the system dynamics.
3. Stationary transition probabilities: Markov chains assume that the transition probabilities do not change over time. In reality, the transition probabilities might be time-varying or influenced by other factors, which may not be adequately modeled by a Markov chain.
4. Finite-state space: Markov chains are typically applicable to systems with a finite number of states. For systems with a large or infinite state space, other modeling techniques are more appropriate.
5. Lack of exploration: Markov chains are not exploratory models, meaning they cannot actively search for new states or patterns outside of the known state space. They are confined to the states defined at the beginning and cannot discover new states during the analysis.
6. No consideration of external factors: Markov chains do not incorporate external influences or contextual information beyond the state-transition probabilities. They focus solely on the internal dynamics of the system and do not capture the impact of external events or factors on the system.
It is important to consider these limitations when applying Markov chains to real-world problems and to explore alternative modeling techniques when these limitations are not suitable for a specific scenario.