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IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant

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Getting started with Bayesian methods

In the last recipe, we used a frequentist method to test a hypothesis on incomplete data. Here, we will see an alternative approach based on Bayesian theory. The main idea is to consider that unknown parameters are random variables, just like the variables describing the experiment. Prior knowledge about the parameters is integrated into the model. This knowledge is updated as more and more data is observed.

Frequentists and Bayesians interpret probabilities differently. Frequentists interpret a probability as a limit of frequencies when the number of samples tends to infinity. Bayesians interpret it as a belief; this belief is updated as more and more data is observed.

Here, we revisit the previous coin flipping ...

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