Great!👍 🙏
Background (I needed a reminder myself 😅):
Bayes’ Theorem provides a way to update the probability of a hypothesis based on new evidence. It combines prior knowledge (initial belief about the hypothesis) with new data to give a more accurate probability.
Example:
Very intuitive explanation Akshay. If i may add Bayes theorem is very important segment of Statistics (for audience, it important to differentiate Bayes theorem and Bayesian Statistics)
great viz; this comes to mind:
"correlation != causation"
ok, what does then?
Pearl's do-calculus is a solid attempt, I'd love to see a similar graphic for it
Great. Although I like it more in the form P(A | B) P(B) = P(B | A) P(A), because you can see the symmetry and also avoids denominators.
In your drawing, both sides equal the purple piece 😊
After randomly drawing 9 black balls from a dark bag, what is the probability that the remaining ball to be drawn is white?
Note: the probability that there are balls of two colors in the bag is 1/3, 1/3 all white 1/3 all black.
