3.4 How has the model changed from last week ?

The example of Kasparov’s probability of beating Deep Blue at chess was a discrete example.

In that case, we greatly over-simplified reality to fit within the framework of introductory Bayesian models. Mainly, we assumed that $\pi$ could only be 0.2, 0.5, or 0.8, the corresponding chances of which were defined by a discrete probability model.

• However, in the reality of Michelle’s election support and Kasparov’s chess skill, $\pi$ can be any value between 0 and 1. We can reflect this reality and conduct a more nuanced Bayesian analysis by constructing a continuous prior probability model.

So ….

• Probability density functions for continuous models

• rather than

• Probability mass functions for discrete models