Markov Chains Explained Visually
by Victor Powell, Lewis Lehe · Setosa (Explained Visually)
Interactive explainer that animates states hopping between nodes, then builds the transition matrix from those diagrams. Readers can edit the matrix and watch how a chain's long-run behavior changes. It works as a short, intuition-first first exposure before formal treatment.
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Harvard Stat 110 lecture that defines the Markov property, transition matrices, and n-step probabilities via matrix powers, then introduces stationary distributions. Lectures 32 and 33 continue with irreducibility, recurrence, reversibility, and random walks on graphs.
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