Timeline for Bayesian approach to interval prediction?
Current License: CC BY-SA 4.0
6 events
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| Apr 5, 2019 at 17:04 | comment | added | Demetri Pananos | The poisson has non-zero mass on all the naturals, not JUST 0 or 1. The poisson may be used to model counts in a specific interval, but it can also be used to model counts in general. You can consider the time between transactions as a single interval, and thus you are counting days in that interval. If you are counting the number of days between transactions, that seems like a reasonable use of the poisson distribution to me, but again you are free to change the likelihood to another discrete distribution. The meat of this answer is the hierarchical structure, not the likelihood. | |
| Apr 5, 2019 at 16:53 | comment | added | Jonathan | I did review the properties, those quotes are straight from the wiki page. I may be misinterpreting, but it seems that the poisson distribution is focused on counting the number of occurrences in a fixed interval. Again, I want to know the expected interval until next single occurrence. Are you able to qualify your suggestion any further? PS- definitely 1 (and by some definitions 0) are both natural numbers, and are acceptable values of k | |
| Apr 4, 2019 at 23:15 | comment | added | Demetri Pananos | The Poisson is supported on the natural numbers, not 0 or 1. I don't know where you are getting that from. The model I've listed posits that the days between transactions (a natural number) is Poisson distributed. That is a pretty innocuous and standard assumption for integer data. I would suggest you review the properties of density functions before continuing. | |
| Apr 4, 2019 at 20:48 | comment | added | Jonathan | Thank you for the input! Refreshing my memory on Poission distributions, am concerned that the assumptions don't fit my use case: "k is the number of times an event occurs in an interval and k can take values 0, 1," Does this apply, since k will always be 1? I think i want to predict the interval itself. "The rate at which events occur is constant. The rate cannot be higher in some intervals and lower in other intervals" ^ this is almost definitely not the case for me. The rate will vary as a function of the customer's age. | |
| Apr 4, 2019 at 20:36 | comment | added | Demetri Pananos | You can switch out the likelihood to whatever fits your assumptions best, | |
| Apr 4, 2019 at 18:19 | history | answered | Demetri Pananos | CC BY-SA 4.0 |