@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.

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
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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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