Many years ago, I was a systematist, working on the taxonomy of an obscure group of flatworms (long story, details at some later date). In any case, I was using morphological characters to construct the phylogeny, and one of the challenges was to assign reasonable weights to the characters to reflect their propensity to change over evolutionary time. More complex characters would have a greater inertia to change, compared to simpler characters. People had devised various schemes, both subjective and objective, but these methods required weight assignments across all characters. But my problem was more general — suppose, for three characters A, B and C, you knew that both B and C could change more frequently than A, but you had no idea how B and C related to each other. How would you assign weights in this case? I developed a method that I called “information-rich character weighting”, and I applied it to parsimony analysis.The procedure was an iterative method that used weights derived from the character consistency indices.

Of course, I didn’t realize it at the time, but what I was doing was clumsily trying to couch the problem in a Bayesian framework: in the paper, I argue that the relative weights are a reflection of prior belief. Which leads me to this project. In Bayesian phylogenetic inference, is there a way of specifying incompletely-specified conditional priors on the relative rates of change of different characters? By this I mean that, as with the example of A, B and C above, I have some prior belief that B and C evolve more rapidly than A, but I have no prior belief about the relative rates of B and C. How can I implement this in a Bayesian phylogenetic analysis?