That’s where the « null hypothesis » comes in. If it fixes the parameters in the model you get a well-defined « null » model with a well-defined probability distribution for the observation and - just like you can take this null hypothesis model and do frequentist calculations with it - you can take this model and calculate a Bayes factor relative to some other model.

(To be clear, if the null hypothesis doesn’t fully specify the parameters the preceding paragraph doesnt apply and the situation is more complex.)