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RichardKennaway comments on Comments on "When Bayesian Inference Shatters"? - Less Wrong Discussion

8 Post author: Crystalist 07 January 2015 10:56PM

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Comment author: RichardKennaway 09 January 2015 07:15:57PM 0 points [-]

My impression is that implicitly relying on arbitrary precision of a prior can give updates that are diametrically opposed to the ones you'd get with different, but arbitrarily similar priors.

I'm not sure what the "precision of a prior" means. A prior is an expression of the knowledge you have before obtaining the data. It is not something that is a measurement of something else, which it could be a more or less precise measurement of.

Has anyone produced a scenario in which the brittleness phenomenon arises in realistic practice?

Comment author: Anders_H 12 January 2015 10:10:25PM *  0 points [-]

Precision is the reciprocal of the variance. In other words, you can use it as a measure of spread. If you are relatively certain that the true value of a parameter is in a narrow range, your prior will have low variance / high precision. If you think the true value may lie in a broader range, you have high variance / low precision.

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