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I want to know how I can model a distribution that is basically n Bernoulli trials. However, these Bernoulli trials can have different probabilities and outcomes.

It is basically the same question as asked here: Probability distribution for different probabilities, with the addition of the possible different outcomes.

The eventual goal is to give a confidence interval on a measure of a set of n predicted revenue streams with it's own size and probability.

Is it even possible to define a confidence interval or define a distribution when the individual Bernoulli trials that make it up have different possible outcomes?

Joep
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  • can you clarify what you mean by "different outcomes"? – Glen_b Oct 27 '20 at 10:05
  • It's certainly possible to define confidence intervals and distributions for this case, but there's unlikely to be a simple formula for them. I don't think there's much to say here in the absence of more information on the probabilities in the Bernoulli trials. – Matt F. Nov 01 '20 at 19:57
  • See also: https://stats.stackexchange.com/questions/494302/high-dimensional-bernoulli-factory/494539#494539 – Peter O. Nov 02 '20 at 01:55

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