Thomas Bayes was a 17th century English polymath. He documented a method for statistical analysis and probability which has become core to those disciplines.

In its simplest form, Bayes Theorem allows us to find the probability of an event occurring them we know the probability of multiple related events occurring.

- If we know that A leads to D Y% of the time
- And we know that B leads to D X% of the time
- And we also know that if C occurs, then D did
*not* occur Z% of the time
- So, if A, B, and C all occur, we can now calculate how likely it is that D will occur

Roughly speaking, Bayes Theorem allows us to predict the future by knowing the past.

If we know that when a country’s inflation rate hits 30%, that country was 45% like to have a coup in the next 30 days; and when a country has a significant natural disaster, it was 3% likely to have a coup in the next 30 days; then we can predict the likelihood of a coup when those two things happen together.

Bayes Theorem is a specific mathematical equation. I won’t reproduce the math here because it’s easily findable and well-known.

The word “Bayesian” is used in a lot of ways – “Bayesian analysis” and “Bayesian inference” are both popular. In essence, these refer to the same thing: using the probability of known past events to predict the likelihood of a future event.