This paper presents a new method for probabilistic modelling of space, called the Bayesian Map for- malism. It offers a generalization of some com- mon approaches found in the literature, as it does not constrain the dependency structure of the prob- abilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction Operator. In the resulting hierarchy, lo- calization in the high level model is based on prob- abilistic competition of the lower level models. Ex- perimental results validate the concept, and hint at its usefulness for large scale scenarios.