ntly simplifies marginal likelihood
estimation via bridge sampling). Furthermore, the marginalization allows
us to properly specify selection model publication bias adjustment
models – the marginalization propagates the selection process up through
all the sampling steps at once (we cannot proceed with the sequential
sampling as the selection procedure on the observed effect sizes
modifies the sampling distributions of all the preceding levels).</p>
<p>We can further re-parameterize the model by performin