As I mentioned in the post "Feynman Technique", I would like to try to explain some of the concepts with plain and simple words as much as possible to test my understanding as if I explain it to my kid.

The first one is "Bayes Theorem."

### Bayes theorem

Bayes theorem helps us to calculate the probability of what happen**ed in the past based on the current findings. **

For example, hey kid, let's say you would like to know how mom's mood was today based on her face and her activity now after you come back from school. If she was in a good mood, she is "going out" with 80% probability, 8 times out of 10 times. On the other hand, if she was in a bad mood, she is "staying home" with 70% chances, 7 times out of 10 times. Overall she is in a good mood with 90%. In this setting, if you see your mom is coming from outside and see she was going out, how likely do you think she was and is in a good mood?

- The event of "she is going out" 8 / 10 and "in a good mood" 9/10 = 72 / 100
- The event of "she is going out" 8 / 10 but "in a bad mood" 1 - 9/10 = 8 / 100

So if you add up the two type of events, the chance of "going out" is 72 / 100 + 8/100 = 80/100.

Within this event, the ratio of the even when she is in a good mood" or "in a bad mood" is (72/100) ➗ (80/100) = 72/80 = 9/10. So you see that she was going out, more likely she is in a good mood. Does it match with your intuition?

I hope my kid will understand with this explanation.

When reading my writing above, the shaky area or one using a lot of technical terms is around probability and math. Also, I can add a more good explanation for how to calculate the ratio of she was going out.

Let me know where you think the shaky area is.

- Hiro

@hiro Sorry, I still didn't get it ??♂️

@gabrielgreco I had 0.1 seconds of bitter feeling since I believed I made a good example but right afterwards I was getting to appreciated that you made a comment and tried to read my post... this is the starting point and will see how much I can improve.

@hiro To be fair, I'm not very good at mathematical concepts and models, so don't take it to heart.