Interpreting the posterior

Recall that a Prior of (0.5, 0.5) reflected our prior opinions about the chance of Spinner A and Spinner B.

After observing a blue spin, the Bayes' rule calculations are summarized in the following data frame, bayes_df:

      Model Prior Likelihood    Product Posterior
1 Spinner A   0.5  0.5000000 0.25000000      0.75
2 Spinner B   0.5  0.1666667 0.08333333      0.25

You can use the function prior_post_plot() from the TeachBayes package to display the prior and posterior probabilities:

prior_post_plot(bayes_df)

Which statement reflects your current beliefs about the identity of the unknown spinner?