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Are conjugate priors required when performing Gibbs sampling?

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    $\begingroup$ No. Only conditionally conjugate priors are required. $\endgroup$
    – mef
    Jun 9, 2016 at 1:08
  • $\begingroup$ By conditionally conjugate priors, do you mean that we can mathematically determine the unnormalized conditional posterior distribution and draw from it? $\endgroup$
    – Ijies
    Jun 9, 2016 at 1:16
  • $\begingroup$ No. Only that the prior be conjugate for the conditional distributions (of which the Gibbs sampler is composed). $\endgroup$
    – mef
    Jun 9, 2016 at 1:20

1 Answer 1

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You don't require conjugate priors for Gibbs sampling.

What you need to be able to do is produce samples from the full conditional distributions.

Conjugate priors generally make that easier, but easier is not the same as necessary.

In particular situations there may be any of a variety of ways of producing samples from the full conditionals without a conjugate prior.

For example, one approach that is sometimes usable is via accept-reject; if you can bound the ratio of an approximation you can sample from the prior x likelihood, that may be doable (e.g. if you can bound the ratio of the density of some mixture of conjugate priors to the actual prior).

Of course, possible doesn't necessarily imply its always the best available solution to the issue.

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  • $\begingroup$ Sorry for the very late reply but Can you please explain to me what you mean by "if you can bound the ratio of an approximation you can sample from to prior x likelihood"? Does that solution or algorithm have a name so that I can read about it more? $\endgroup$
    – floyd
    Aug 26, 2019 at 7:13
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    $\begingroup$ There's a typo in there (which I just fixed). 'to' should be 'the'. I'll see if I can explain in more detail. $\endgroup$
    – Glen_b
    Aug 26, 2019 at 7:38

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