3
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I intend to use a hierarchical binomial model which would look like the following in BUGS language :

model{
for(i in 1:I){
   y[i] ~ dbin(theta[k[i]],n[k[i]])
}
for(j in 1:J){
   theta[j] ~ dprior(alpha, beta)
}
alpha ~ dhyperprior.alpha
beta ~ dhyperprior.beta
}

I wonder about the choice of the prior on $\theta$ and the hyperpriors. Do you know how to do this choice in order that:

1) There's no need to use MCMC (classical simulations suffice)

and/or

2) This is a good "noninformative" choice, in the sense of the frequentist-matching property (the frequentist coverage of the posterior credible intervals is close to the credibility level)

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  • 1
    $\begingroup$ BTW, as Marc Kery says, no model is Bayesian :-) Bayesian is only the way of inference. $\endgroup$ – Curious Mar 13 '12 at 18:25
4
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In answer to (1):

Set

$\theta_i \sim \text{Beta}(\alpha, \beta)$

Then the marginal data distribution, $\int_0^1\Pr(y_i|\theta_i)p(\theta_i|\alpha, \beta)d\theta_i$, is a standard distribution:

$y_i \sim \text{Beta-binomial}(\alpha, \beta)$

(Wikipedia article: Beta-binomial distribution; see especially the section on "Further Bayesian considerations".) Since this results in a bivariate posterior distribution, you can easily evaluate it on a finely-spaced grid; thus MCMC can be avoided for any choice of hyperpriors.

More specifically, you can find the region of high posterior density by numerical optimization, using the ML estimate as the initial guess. After finding the region containing all but a negligible amount of posterior probability mass and evaluating the posterior density on the fine grid, you can generate random $(\alpha, \beta)$ pairs by picking one of the points on the grid with probability proportional to the evaluated density and then adding uniform jitter centered at the selected point with width equal to the grid spacing.

If you're interested in inferring the $\theta_i$ variables, you can sample them by first sampling a set of $(\alpha, \beta)$ values from their posterior and then sampling the $\theta_i$ from their conditional posteriors,

$\theta_i|y_i, \alpha, \beta \sim \text{Beta}(\alpha + y_i, \beta + n_i - y_i)$

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  • $\begingroup$ Thanks. I easily understand the Beta-binomial step. But I do not understand what you say about the "grid". Do you know some reference about this step ? $\endgroup$ – Stéphane Laurent Mar 13 '12 at 7:45
  • $\begingroup$ Yup. Gelman et al., Bayesian Data Analysis (2nd ed.) has an example of this exact model analyzed in this way starting on page 118 (section 5.1) and continued on page 127. The continuation on page 127 discusses the posterior sampling. $\endgroup$ – Cyan Mar 13 '12 at 13:26
  • $\begingroup$ Correction: an earlier example starting on the bottom of page 88 describes this approach to posterior sampling for bivariate posterior distributions in detail. $\endgroup$ – Cyan Mar 13 '12 at 13:52
-1
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For my binomial models, I use normal priors - on the logit scale:

for (...) {
    y[i, j, ...] ~ dbin(p, M[i])
    ....
}

# priors
p <- 1/(1+exp(-logit_p))
logit_p ~ dnorm(0, tau)
tau <- 1/(4 * 4)

But note that I'm not an expert, on the contrary - I just considered this prior to be better than usual uniform (p ~ dunif(0, 1)). I plan to try better prior mentioned at http://psiexp.ss.uci.edu/research/programs_data/jags/Example3.html#1

model {
   # Prior on Rate
   theta ~ dbeta(1,1)
   # Observed Counts
   k ~ dbin(theta,n)
}

Which quite corresponds to what Cyan is proposing in his answer.

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  • $\begingroup$ This is really not an answer to my question: there is no hyperprior in your model. $\endgroup$ – Stéphane Laurent Mar 13 '12 at 20:30
  • $\begingroup$ @StéphaneLaurent, OK, I see. But maybe in the first example you could make tau a hyperprior... $\endgroup$ – Curious Mar 13 '12 at 20:52
  • $\begingroup$ And the question is in particular about the choice of this hyperprior ! $\endgroup$ – Stéphane Laurent Mar 14 '12 at 18:56

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