@techwith_ram: This is a great lecture at MIT by David Shirokoff on Markov Chains. He covers the fundamentals of Markov Chains using a…
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A tweet shares a lecture at MIT by David Shirokoff covering the fundamentals of Markov Chains, including transition probabilities, Markov matrices, eigenvalues, and long-term steady state.
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Cached at: 05/23/26, 04:14 PM
This is a great lecture at MIT by David Shirokoff on Markov Chains.
He covers the fundamentals of Markov Chains using a simple particle movement example.
He starts by explaining how a particle moves between two positions, A & B, with different probabilities. From there, the talk converts the problem into matrix form using a Markov matrix.
The main topics covered are:
- Transition probabilities
- Markov matrices
- Probability vectors
- Matrix multiplication in Markov Chains
- Finding probabilities after n steps
- Eigenvalues and eigenvectors
- Matrix diagonalization
- Long-term steady state distribution
𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 (@techwith_ram): Can you predict the future…. by only knowing the present??
I will try to explain here the strange power of Markov’s Chains.
Most systems around us follow patterns. Not exact rules, but probabilities. That’s also the core idea behind a Markov chain.
Its a mathematical model
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