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You can’t have everything you want: beta edition
The beta distribution is a conjugate prior for a binomial likelihood function, so it makes posterior probability calculations trivial: you simply add your data to the distribution parameters. If you start with a beta(α, β) prior distribution on a proportion θ, then observe s successes and f failures, the posterior distribution on θ is beta(α +
Making the two-dimensional one-dimensional
We often want to reduce something that's inherently two-dimensional into something one-dimensional. We want to turn graph into a list. And we'd like to do this with some kind of faithfulness. We'd like things that are close together in 2D space to be close together in their 1D representation, and vice versa, to the extent