Markov Chains and Their Applications

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.