Mixed Poisson distribution
Okay kiddo, so do you remember what a regular Poisson distribution is? It's like when we count how many times something happens in a certain time period. Like how many cars drive by in an hour or how many people visit a store in a day.
Well, a mixed Poisson distribution is a little different. It's still like counting things, but now we have different time periods or situations where things happen with different frequencies. It's like if we're counting the number of red and green balls in a bag, but the number of times we reach in the bag changes depending on if we're hungry or not.
So in a mixed Poisson distribution, the frequency of something happening isn't always the same. We have different “rates” and we use these rates to calculate the probabilities of something happening at any given time.
Imagine we have a bag full of red and green balls, but we only have time to reach in the bag three times. The first time we reach in, we're really hungry, so we grab a lot of balls. The second time, we're not so hungry, so we only grab a few balls. The third time, we're really tired, so we don't even reach in the bag at all. The frequency of things happening in these different moments is different, so it makes a mixed Poisson distribution.
So the mixed Poisson distribution is useful because it can help us understand and predict the likelihood of things happening in different situations, even if those situations happen with different frequencies over time.
Well, a mixed Poisson distribution is a little different. It's still like counting things, but now we have different time periods or situations where things happen with different frequencies. It's like if we're counting the number of red and green balls in a bag, but the number of times we reach in the bag changes depending on if we're hungry or not.
So in a mixed Poisson distribution, the frequency of something happening isn't always the same. We have different “rates” and we use these rates to calculate the probabilities of something happening at any given time.
Imagine we have a bag full of red and green balls, but we only have time to reach in the bag three times. The first time we reach in, we're really hungry, so we grab a lot of balls. The second time, we're not so hungry, so we only grab a few balls. The third time, we're really tired, so we don't even reach in the bag at all. The frequency of things happening in these different moments is different, so it makes a mixed Poisson distribution.
So the mixed Poisson distribution is useful because it can help us understand and predict the likelihood of things happening in different situations, even if those situations happen with different frequencies over time.