# Can every parameter $\Theta$ in Bayesian modelling be explained via De Finettis representation theorem

My question is the following: I recently got to know (and love) De Finettis representation theorem and I now started to read a Book an Bayesian statistics. However this book simply takes as the starting point of Bayesian analysis a statistic model with density $$f(x|\theta), \theta \in \Theta$$, and does not mention De Finetti's representation theorem at all. I wonder now whether the parameter $$\Theta$$ can always (as long as we deal with exchangeability) thought of as being justified by De Finetti's representation theorem?