Abstracting Probabilistic Models: Relations, Constraints and Beyond

Research output: Contribution to journalArticlepeer-review


Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood.

In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational, deterministic and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We go on to prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.
Original languageEnglish
Article number105976
Number of pages26
JournalKnowledge-Based Systems
Early online date30 Apr 2020
Publication statusPublished - 8 Jul 2020

Fingerprint Dive into the research topics of 'Abstracting Probabilistic Models: Relations, Constraints and Beyond'. Together they form a unique fingerprint.

Cite this