Timeline for Inferring prior distribution
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Sep 4, 2014 at 13:46 | history | edited | Cam.Davidson.Pilon |
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| Sep 4, 2014 at 3:39 | vote | accept | Uwat | ||
| Sep 3, 2014 at 18:19 | answer | added | Cam.Davidson.Pilon | timeline score: 7 | |
| Sep 3, 2014 at 18:03 | comment | added | Cam.Davidson.Pilon | This sounds like a hierarchical model. If I wanted to recreate the dataset, here's what I'd do: Let $D$ be $Beta(\alpha, \beta)$ (reasonable since we are dealing with probabilities). We don't know $\alpha, \beta$, so we assign priors to them, say exponential for both with some $\lambda$ hyperparameter. Then we draw the $p_i$ for each $i$, and sample $X_i$ from the binomials. Let me write something up... | |
| Sep 3, 2014 at 17:54 | answer | added | Tom Minka | timeline score: 3 | |
| Sep 3, 2014 at 4:42 | comment | added | shadowtalker | One thing to keep in mind is that a prior is supposed to be (at least in principle) information not contained in the data but that you have available through some other channel (previous study, intuition, etc). Obviously that raises a bigger issue about empirical priors in general, but right now it sounds like you just don't have much outside info. So why not use some kind of weak prior? You could use the beta distribution, which happens to be conjugate, but is also very flexible. Then you're also free to control the tails and apply hyperpriors (e.g. the half-Cauchy). | |
| Sep 3, 2014 at 3:53 | history | edited | Uwat | CC BY-SA 3.0 |
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| Sep 3, 2014 at 3:32 | history | asked | Uwat | CC BY-SA 3.0 |