Timeline for Accounting for uncertain information (few observations) in a prior (empirial Bayes)

4 events

when toggle format	what		by	license	comment
Jun 12, 2023 at 13:39	vote	accept	Damiaan Reijnaers
Dec 8, 2019 at 10:51	comment	added	jkm		You would always have a weighted mixture of two (in this example) Action priors contributing to the posterior. The mixing weights are proportional to how well the number of observations fits each action priors clusters' counts. E.g. a person with 100 observations would have 99% $Prior_{hi}$ and 1% $Prior_{lo}$, a person with 3 would have a blend of 80% low and 20% high, someone with 20 would have a 50:50 mix (so just plain arithmetic mean).
Dec 7, 2019 at 17:20	comment	added	Damiaan Reijnaers		That is a step in the good direction! The problem is that the majority of individuals in the data set have just 1-5 observations. (The fact that they have such less observations adds some information useful to estimating their $p(Action\|Prior)$.) But, if I would construct a prior based on only these type of persons (with few observations), wouldn't too many of them (in the prior Beta dist.) have values like '100', '0' or '80' (highly unlikely values)?(1) And this way, a new person will almost always fall into the 'low observations' cluster, in the beginning, right?(2) Am I missing something?
Dec 7, 2019 at 16:54	history	answered	jkm	CC BY-SA 4.0