Initial README commit

Author: ft-adm

README.md

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+Entropy Pooling in Python
+=========================
+
+Due to popular demand from developers, this package contains the Entropy Pooling
+implementation from the [fortitudo.tech Python package](https://github.com/fortitudo-tech/fortitudo.tech)
+with a more permissive BSD 3-Clause license.
+
+This package contains only one function called ep and has minimal dependencies
+with just scipy. See [this example](https://github.com/fortitudo-tech/entropy-pooling/blob/main/example/EntropyPooling.ipynb)
+for how you can import and use the ep function.
+
+Installation instructions
+-------------------------
+
+Installation can be done via pip:
+
+    pip install entropy-pooling
+
+Theory
+------
+Entropy Pooling is a powerful method for implementing subjective views and
+performing stress-tests for fully general Monte Carlo distributions. It was first
+introduced by [Meucci (2008)](https://ssrn.com/abstract=1213325) and refined
+with sequential algorithms by [Vorobets (2021)](https://ssrn.com/abstract=3936392).
+
+The original Entropy Pooling approach solves the minimum relative entropy problem
+
+$$q=\text{argmin}\lbrace x'\left(\ln x-\ln p\right)\rbrace$$
+
+subject to the constraints
+
+$$Ax=b \quad \text{and} \quad Gx\leq h.$$
+
+The constraints matrices $A$ and $G$ contain transformations of the Monte Carlo
+simulation that allow you to implement subjective views and stress-tests by
+changing the joint scenario probabilities from a prior probability vector $p$
+to a posterior probability vector $q$.
+
+A useful statistic when working with Entropy Pooling is the effective number of
+scenarios introduced by [Meucci (2012)](https://ssrn.com/abstract=1971808). For
+a causal Bayesian nets overlay on top of Entropy Pooling, see
+[Vorobets (2023)](https://ssrn.com/abstract=4444291).