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A discrete Markov chain is basically a random walk on a graph, where each outgoing edge has a fixed probability. Cool fact: any initial distribution on a sufficiently nice* Markov chain converges to its stationary distribution after many steps—because it's a contraction mapping.

Keenan Crane

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181,771 views • 1 year ago •via X (Twitter)

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One of the simplest algorithms for sampling from a probability distribution is Random Walk Metropolis-Hastings. It proposes new samples by taking Gaussian-distributed steps, accepting or rejecting them to maintain the target distribution. I call this pdf the "fidget spinner".

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Institutions want to build on Ethereum and leverage its incorruptibility, hard finality, and global liquidity. But many financial use-cases can’t be run on a chain where everything is public by default. Here’s how Prividium extends Ethereum for enterprises:

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