= Solution
Model B replaces independent tree parameters by <multivariate normal> <random effects>. It permits correlated asymptote, timing and slope variation and supplies <partial pooling>, a useful improvement if the five trees can be regarded as <exchangeable>.
The <Wishart distribution> prior is on the <precision matrix> $\Omega$, not the covariance matrix. In the <WinBUGS> convention, dimension three and degrees of freedom three give a proper but very dispersed prior. The induced <inverse-Wishart distribution> has no finite mean at those degrees of freedom. Thus this is not a neutral choice of covariance prior, and estimating six covariance components from only five trees is weakly informed. The broad mean priors and possible negative growth slopes also remain. Check prior-predictive curves and sensitivity, and consider separate, interpretable scale and correlation priors.
The resulting posterior means of the second and third parameters are quite similar across trees, motivating a simpler shared-shape model; their uncertainty means this similarity is evidence to explore, rather than a proof of exact equality.
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