Carnapian inductive logic is an instance of probabilistic logic, because its sequential predictions are all based on a single probability assignment at the outset, and because it relies on Bayes' theorem to adapt the predictions to sample data (cf. Romeijn 2011). One important difference with Bayesian statistical inference is that, for Carnap, the probability assignment specified at the outset only ranges over samples and not over hypotheses. However, by De Finetti's representation theorem Carnap's exchangeable rules can be equated to particular Bayesian statistical inferences. A further difference is that Carnapian inductive logic gives preferred status to particular exchangeable rules. In view of De Finetti's representation theorem, this comes down to the choice for a particular set of preferred priors. As further developed below, Carnapian inductive logic is thus related to objective Bayesian statistics. It is a moot point whether further constraints on the probability assignments can be considered as logical, as Carnap and followers have it, or whether the title of logic is best reserved for the probability formalism in isolation, as De Finetti and followers argue. 