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Mar 11, 2019 at 7:37 comment added Toby @StéphaneLaurent Thank you for the link. I'll look at it
Mar 9, 2019 at 15:07 comment added Stéphane Laurent There is a family of prior distributions which yields nice full conditional posterior distributions for the Gibbs algorithm. You can see the formulas here. The prior distribution used by Gelman & Hill belongs to this family.
Mar 5, 2019 at 7:11 vote accept Toby
Mar 1, 2019 at 14:54 answer added merv timeline score: 1
Feb 27, 2019 at 8:34 comment added Toby Can you explain why the code is correct? I assumed the code to be wrong. It seems comprehensibly to compute sigma.a and sigma.y as discribed in formula 18.13 and 18.15. Why wouldn't you divide by n or J?
Feb 27, 2019 at 8:24 comment added Stéphane Laurent Sorry, I misread yesterday. You are right, there's an error. The code is correct, the error is in the description of the algorithm.
Feb 27, 2019 at 8:06 history edited Stéphane Laurent
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Feb 27, 2019 at 0:16 history migrated from stackoverflow.com (revisions)
Feb 26, 2019 at 13:12 comment added Stéphane Laurent It is better to answer with LaTeX, which is available on CV but not here. They exactly code according to the algorithm they describe ("and then draw ..."). Looks like you are confounding sigma and sigma hat.
Feb 26, 2019 at 12:47 comment added Toby alright could ask the question in CV, but you could just give an answer on the question and next time ill ask on CV. Sounds fair? I think I got an idea on how the gibbs sampler works. It just doesn't make any sense to me, how they describe the algorithm and code different from that
Feb 26, 2019 at 12:39 comment added Stéphane Laurent I have an answer but I voted to close your question because it belongs to Cross Validated (stats.stackechange). Do you know what is the Gibbs sampling ? Gelman & Hill's book do not provide a description of the Gibbs sampling. Each parameter is updated according to its conditional distribution given the other parameters and y.
Feb 26, 2019 at 12:18 history asked Toby CC BY-SA 4.0