Bayesian
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.
Why I Looked It Up
I first heard the word 20 years ago when Bayesian analysis became popular in email spam detection. It went something like this:
If the word “penis” is in an email, there’s a 91% chance it’s spam. If the word “enlargement” is in an email, there’s a 57% chance it’s spam. But if the word “accounting” is in an email, there’s only a 3% chance it’s spam. If all three words occur, what is the chance that it’s spam?
Bayesian analysis was used in most of the original spam suppression systems. The system would “learn” from pre-categorized emails (either your own, or a general domain set that someone had), and then would apply those learnings to incoming email.
I used a Bayesian filter on a Microsoft Exchange server back in 2004 or so, and it was very effective.
Since then, I’ve heard the word Bayesian used in a lot of books on politics and intelligence. It’s quite commonly used by intelligence services to predict the likelihood that some event will occur.
I had a general understanding of why it was used, but I wanted specifics.