Understanding the Differences Between Bayesian and Frequentist ...

Any Bayesian analysis begins with an initial belief, aka a prior. In our case, the prior was the initial guess we had about the location of the target ball. Then we encounter objective information, which in our case was whether the new ball landed to the left or the right of the target ball. When you combine the two it gives you the improved belief

Essentially, Bayes’ big insight was that you must add any new information you get to the information you already have. In this case, you don’t have very much information. But it is something. What that means is that instead of just saying, ‘The most likely position of the line is two-fifths of the way along the table,’ you have to take account of y

The most common way of excluding other relevant differences is through a randomized controlled trial (RCT).

The transparency of Bayesian networks distinguishes them from most other approaches to machine learning, which tend to produce inscrutable “black boxes.” In a Bayesian network you can follow every step and understand how and why each piece of evidence changed the network’s beliefs.

No experiment in the world can deny treatment to an already treated person and compare the two outcomes, so we must import a whole new kind of knowledge.