principal components / eigenvectors are in effect patterns of movement.

and data shows how much each pattern is active at any one time

under classical patterns are fixed .. we say these are the patterns and these are the scores

under bayesian we say we are not entirely what the scores are so lets treat them as uncertain and estimate them

the priors come in : before looking at the data , what do i expect them to look like.

i guess we could go with pure parallel, slope and twist

priors stop overfitting and inject commonsense