How to Build a Markov Chain in Python With NumPy

Markov chains are mathematical models for systems that evolve between states over time, defined by the memoryless Markov property where the future depends only on the present. Using an example of a writer's daily states (Drafting, Revising, Distracted), the core components like the transition probability matrix are explained. The content demonstrates how to calculate multi-step probabilities using the Chapman–Kolmogorov equation, which is simplified through matrix multiplication. It also introduces the concept of a stationary distribution, representing the long-term, stable probabilities of the system being in any given state, and provides a Python implementation using NumPy to compute these values.