This blog post has been created to convince you that real-world probability, is in fact Bayesian probability.

Anyone who believes that a frequentist approach is superior may be correct (for that particular example), but it must be said that the Bayesian framework is a superset of this naive and trivial card-playing model of probability.

We are no longer trying to determine the probability of landing a `double-six` dice roll, and rather we are trying to figure out what the probability is that Mia (our cat) will be waiting for us on the porch when we get home.

History (optional)

It starts with Cardano, leads to Pascal and Leibniz. Makes its way to insert russian dude, blah blah blah.

The Crux of the Matter

Prior. That is the crux. What is the probability Mia will be waiting on the chair on the porch? Well that depends on a few things: is it raining? did we feed her before we left? is it really hot? did she get into another cat fight?

Each of these questions, either answered or unanswered influence the probability.

Formally we have P(Mia on Red Chair) =

Comparisons

Physical Examples

Meta-Physical Examples

The nature of the matter is that black-box, infinitely sampling from this black box, etc. Thus superset.