Markov Chain Monte Carlo (MCMC)

It generates samples, like footprints in snow, tracing out possibilities. One answer? No. It offers many. A spectrum of outcomes, revealing the landscape, not just a single point.

Its strength? Flexibility. No matter how tangled the relationships, no matter how many variables, it works.

Methods like Metropolis-Hastings or Gibbs sampling are just different ways MCMC “walks” through these possibilities, step by step, building a clearer picture. But for most, it’s simply a smart technique to break down problems.

More than just calculations, it simplifies the complex when direct calculations are impossible—turning uncertainty into insight.

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