Exact(1)
Terminations due to non-positive definite genetic covariance matrix occurred in flat prior analyses of the smallest datasets.
Similar(59)
Across all subjects, r took on relatively large values in the first trials, indicating the subject's belief in a "flat" prior; e.g. the subject's displayed little preference for initially believing the coins would appear in any expected location.
For diet (a binary character), we set the α parameter, describing the shape of the distribution, at 1, resulting in a flat prior distribution, i.e. the transition rates from one state to another are not a priori considered as equal (the distribution was discretised into 19 categories).
We set λ0 to the mean of observed intensities and κ0 to 10−, resulting in a fairly flat prior over the region of interest.
In general flat priors are chosen in the R-package survBayes.
Convergence problems were encountered in > 50% of flat prior analyses, with indications of potential or near posterior impropriety between about round 10 000 and 100 000.
These four variances are estimated in the model using flat prior distributions, i.e., σ u 2, σ s 2, σ g 2, σ e 2 ∼ Bern.
This algorithm produces progressively more accurate estimates of each parameter over several rounds; Figure 4 figure supplement 1 illustrates that, for most parameters, round one restricts the credible range of each parameter in comparison to the flat prior and round two leads to further focusing.
In the case of σ e 2, we assigned a flat prior as in Wang et al. (1994).
Because there is maximal uncertainty about the a priori spatial locations of CMBs, that is, they may appear anywhere in the brain [1], [19], a flat prior was used (CMB in Figure 3B).
The model was run for 50,000 iterations with a thinning period of 50 and a burn-in of 20,000 iterations, specifying a flat prior and a Gaussian error structure.
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