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Bayesian Data Science by Simulation


This tutorial is an Introduction to Bayesian data science through the lens of simulation or hacker statistics. We will become familiar with many common probability distributions through i) matching them to real-world stories & ii) simulating them. We will work with joint/conditional probabilities, Bayes Theorem, prior/posterior distributions and likelihoods, while seeing their applications in real-world data analyses. We’ll see the utility of Bayesian inference in parameter estimation and comparing groups and we’ll wrap up with a dive into the wonderful world of probabilistic programming.

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