Let us consider the following Hierarchical Bayesian model: w \sim\ Beta(20, 20) K = 6 a = w * (K - 2) + 1 b = (1 - w) * (K - 2) + 1 theta \sim\ Beta(a, b) y \sim\ Bern(theta) The above is the example of figure 9.3 in the book Doing Bayesian Data Analysis by John Kruschke. I use the pymc3 to cons…

You can indeed just set w=0.25 and re-sample the model. In this case the model is simple and sampling is cheap, so it’s probably the way to go. I just wanted to add that if the model were expensive to sample, you could also get conditional probabilities from the joint posterior using boolean masks. …

Samples from Conditional Posterior Distribution

Questions v3

cluhmann April 6, 2022, 3:33am 2

Can you just set w=0.25?

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Samples from Conditional Posterior Distribution

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