Bayesian poisoning

Okay, so let's imagine you have a jar of jellybeans, and you want to find out how many of these jellybeans are red. Now, you don't know how many red jellybeans there are in the jar, but you want to make a guess anyway. You start by taking out a few jellybeans from the jar and you notice that three of them are red out of ten. Based on what you have guessed so far, you think that there might be 30% of red jellybeans in this jar.

However, someone is trying to trick you, and they add some more red jellybeans to the jar when you're not looking. They are trying to throw off your guess by making it seem like there are more red jellybeans in the jar than there actually are. This is called Bayesian poisoning.

Now, because of the extra red jellybeans in the jar, if you take out another ten jellybeans and count them, five of them are red. This makes you change your guess about how many red jellybeans are in the jar. You now think it might be around 50%.

But here's where things get tricky. You don't know for sure if the extra red jellybeans were added to trick you or if they were added by mistake. So, you have to be careful about how much you change your guess. You don't want to just keep increasing your guess every time you count jellybeans because that would mean you're falling for the poisoning and you're not being accurate.

The best thing to do is to keep track of how many jellybeans you have counted overall and how many of them were red. Then, you can use some math to figure out the probability of how many red jellybeans are in the jar, taking into account the possibility of poisoning. That way, even if someone tries to trick you, you can still make an educated guess about how many red jellybeans there are.