Definitions
Markov chains are mathematical systems that undergo transitions from one state to another, based on certain probabilistic rules. They have numerous applications across various fields.
Key Concepts
- States: The different conditions or statuses that the system can occupy.
- Transition Probabilities: The probabilities associated with moving from one state to another.
Applications
- Machine Learning: Used in algorithms for natural language processing and reinforcement learning.
- Economics: Model consumer behavior and market dynamics.
Understanding Markov chains can significantly enhance your analytical capabilities in stochastic processes.