Introduction to Bayesian Inference

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Originally published on Towards AI.

A Distribution With No Constraints

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In this article, I will explain what the maximum entropy principle is, how to apply it and why it’s useful in the context of Bayesian inference.

The code to reproduce the results and figures can be found in this notebook.

The maximum entropy principle is a method to create probability distributions that is most consistent with a given set of assumptions and nothing more. The rest of the article will explain what this means.

First, we need to a way to measure the uncertainty in a probability distribution. We will use entropy to measure this uncertainty, which is defined as follows:

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