📊 Dive into Markov Chains!

Introduction to Markov Chains

Markov Chains are mathematical systems that undergo transitions from one state to another on a state space. These transitions are probabilistic, and the next state solely depends on the current state and not on the sequence of events that preceded it.

Here's a simple example:

                State 1 ➡️ State 2 ➡️ State 3
                Probability: 0.2        0.5
            

Markov Chains are widely used in various fields like economics, game theory, and even in creating predictive text models!

Learn More 🌟

For a deeper dive into how Markov Chains work and their applications, feel free to check out these resources:

• Understanding Stochastic Processes
• Basics of Probability Theory
• Wikipedia: Markov Chain