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@@ -29,13 +29,19 @@ Entropy Pooling is a powerful method for implementing subjective views and
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performing stress-tests for fully general Monte Carlo distributions. It was first
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introduced by [Meucci (2008)](https://ssrn.com/abstract=1213325) and refined
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with sequential algorithms by [Vorobets (2021)](https://ssrn.com/abstract=3936392).
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[You can loosely think about Entropy Pooling as a generalization of the Black-Litterman model](https://antonvorobets.substack.com/p/entropy-pooling-vs-black-litterman-abb608b810cd) without all the oversimplifying assumptions. Entropy Pooling operates directly on
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[the next generation market representation](https://youtu.be/4ESigySdGf8?si=yWYuP9te1K1RBU7j&t=46)
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defined by the simulation matrix $R\in \mathbb{R}^{S\times I}$ and associated joint
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scenario probability vector $p\in \mathbb{R}^{S}$.
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For a quick introduction to Entropy Pooling intuition, watch [this YouTube video](https://youtu.be/qk_5l4ICXfY).
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The original Entropy Pooling approach solves the minimum relative entropy problem
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