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What Would Success Look Like? (Latent Track)

The aim

With uninformative priors, the Bayesian PCA (Latent) model should largely reproduce what classical PCA already found — that's the point of the "comparing output" step in the Aims plan. Concretely: does the classical loading matrix sit somewhere near the centre of the posterior this model produces, rather than out in the tails?

If it does, that's a basic consistency check passing: the Bayesian machinery isn't distorting the answer relative to the classical one when it isn't given any extra information to justify a different answer. If it doesn't, that's a signal something is wrong — not evidence of a genuinely different, better answer — since no informative prior has been introduced yet to justify moving away from classical.

Why "near the centre" isn't a straightforward column comparison

$\mathbf{W}$ in the latent model is only identified up to rotation and reflection: the likelihood depends on $\mathbf{W}$ only through $\mathbf{W}\mathbf{W}^\top$ (see Where the covariance matrix fits in), so infinitely many rotated/reflected versions of the same $\mathbf{W}$ describe the data identically well. That rules out lining up posterior draws' columns against classical PC1/PC2/PC3 directly — a posterior draw could describe the identical fitted model as classical yet look totally different column by column.

Two ways round this, not yet decided between:

Which of these (or both) gets used is deferred to whichever step first needs to make the comparison.