Suppose I have several point-estimates for the fuel efficiency of my car—I can easily take a weighted average of these to make an aggregate point estimate, but it’s not clear how I could turn them into an interval estimate without a heavy dose of personal bias.
You could create a mixture distribution, you could fit a lognormal whose x% confidence interval is the range expressed by the points you’ve already found, you could use your subjective judgment to come up with a distribution which could fit it, you could use kernel density estimation (https://en.wikipedia.org/wiki/Kernel_density_estimation).
In your number of habitable planets estimate, you have a planetsPerHabitablePlanet estimate. This is an interesting decomposition. I would have looked at the fraction of planets which are habitable, and probably fit a beta distribution to it, given that we know that the fraction is between 0 and 1. This seems a bit like a matter of personal taste, though.
Neat post, and nice to see squiggle in the wild.
Some points
You could create a mixture distribution, you could fit a lognormal whose x% confidence interval is the range expressed by the points you’ve already found, you could use your subjective judgment to come up with a distribution which could fit it, you could use kernel density estimation (https://en.wikipedia.org/wiki/Kernel_density_estimation).
In your number of habitable planets estimate, you have a planetsPerHabitablePlanet estimate. This is an interesting decomposition. I would have looked at the fraction of planets which are habitable, and probably fit a beta distribution to it, given that we know that the fraction is between 0 and 1. This seems a bit like a matter of personal taste, though.