## Ideology

Old day people study probability forcus on sample distribute. [fix parameters]

Bayes probability study forcus on parameters.[fix samples]

1 | prior distribution + sample info -> Posterior distribution |

the new experiment result will refresh people cognition on old things.

It’s fit people cognition law on nation explor method.

## formula

First:

the probability event A, event B happen at the **same time**

, equals the probability **A happen** multiply with the probability**B happen when A happen**.

So we have:

=>

=>

a new knowledge = a priori knowledge * a new factor affect knowledge / a new factor

We use sample experiment’s phenomenon verify rules.

Use `adjust factor`

to adust parameters, make rules more accurate.

## Example

We need to check a rule:

watermelon sugar content > 10, good probability is 70%

- prior condition is good watermelon probability is 20%
- exp:bad watermelon has probability 25%, sugar content > 10
- exp:good watermelon has probability 99%, sugar content > 10

so we have :

Now we found the threshold is not right, base on adjust factor, we change rule:

watermelon sugar content > 15, good probability is 70%

- prior condition is good watermelon probability is 20%
- exp:bad watermelon has probability 15%, sugar content > 15
- exp:good watermelon has probability 90%, sugar content > 15

so we have :

Keep on adjust:

watermelon sugar content > 20, good probability is 70%

- prior condition is good watermelon probability is 20%
- exp:bad watermelon has probability 9%, sugar content > 20
- exp:good watermelon has probability 85%, sugar content > 20