-[Okay, but where do the two types come from? The graph is just showing cause-and-effect, but if this were actually a Bayes net, there would be numbers representing a probability distribution, and I claim that the distribution clusters into two types]
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-[The sexual orientation node increases femininity and decreases AGP, so those pathways are anti-correlated; however, the fact that straight AGP men also vary somewhat in their degree of femininity; some accounts have emphasized how masculine (even hypermasculine) AGPs are, but this seems wrong]
+So where do the two types come from? This graph is just illustrating (conjectured) cause-and-effect relationships, but if we were actually to flesh it out as a complete Bayesian network, there would be additional data that quantitatively specifies what (probability distribution over) values each node takes conditional on the values of its parents. When I claim that Blanchard–Bailey–Lawrence's two-type taxonomy is a useful approximation for this causal model, I'm conjecturing that the distribution represented by this Bayesian network (if we had the complete network) can also be approximated a two-cluster model: _most_ people high in the "femininity" factor will be low in the "autogynephilia" factor and _vice versa_, such that you can buy decent predictive accuracy by casually speaking as if there were two discrete "types".