Bayesian Thinking

Bayesian thinking lets you look at the world with probabilities and update your beliefs in the light of new information. On the contrary, frequentist thinking relies on and requires many observations of an event before it can become helpful.

How does it work? Start by picking a hypothesis. Once you see new evidence, you can update your hypothesis. Let’s say you have a hunch about something being true, but you are unsure. After a few days of observation, you can update how confident you are in the light of new observations. Humans do that all the time naturally. As a mental model, it teaches us to improve what we are doing based on new observations. Rinse and repeat.

A practical example of Bayesian thinking is spam filtering. Knowing what kind of words are in spam, we can predict that a message is spam by looking for those words. In other words, our model predicts the likelihood of spam by looking for particular words. When we see new spam, we can update our model by adding those words — we improve our model.