Bayes' rule is a disciplined way to update belief
New evidence should change a belief in proportion to how likely that evidence was under competing explanations.
Takeaway
Evidence is strongest when it is much more expected under one explanation than another.
What I learned
Bayes' rule describes how to update a prior belief after seeing new evidence. The key idea is not just whether evidence is possible, but whether it is more likely under one explanation than another.
A simple intuition
If an observation would be common when a hypothesis is true and rare when it is false, that observation should increase confidence in the hypothesis.
Why it matters
This is useful for diagnosis, debugging, forecasting, and everyday reasoning. It encourages me to ask: what would I expect to see if each explanation were true?