When are Bayesian methods preferable to Frequentist?

The flip side of the flexibility of the Bayesian approach is that it can also be accused of having too many free parameters.

In many walks of life, expressions of uncertainty are mistaken for admissions of weakness. When you first start to make these probability estimates, they may be quite poor. But there are two pieces of favorable news. First, these estimates are just a starting point: Bayes’s theorem will have you revise and improve them as you encounter new informat

The core of Bayesian thinking (or Bayesian updating, as it can be called) is this: given that we have limited but useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new.

Conditional probability is similar to Bayesian thinking in practice, but comes at it from a different angle. When you use historical events to predict the future, you have to be mindful of the conditions that surrounded that event. Events can be independent, like tossing a coin, or dependent. In the latter case, it means the outcomes of an event ar