Dissertation Abstract: Taming Exact Inference in Temporal Probabilistic Relational Models

Abstract

Processes in our world are of a temporal probabilistic relational nature. An epidemic is an example of such a process. This dissertation abstract uses the scenario of an epidemic to illustrate the lifted dynamic junction tree algorithm (LDJT), which is a temporal probabilistic relational inference algorithm. More specifically, we argue that existing propositional temporal probabilistic inference algorithms are not suited to model an epidemic, i.e., without accounting for the relational part, and present how LDJT uses the relational aspect. Additionally, we illustrate how LDJT preserves groups of indistinguishable objects over time and have a look at LDJT from a theoretical side.

AI in Healthcare and the Public Sector: How to Face the Challenges of High-Risk Applications and What AI Research Can Get Out of It

“…Marcel Gehrke, Johannes Liebenow, Esfandiar Mohammadi, Tanya Braun [5] The aim of privacy-preserving inference is to avoid revealing identifying information about individuals during inference. Lifted probabilistic inference works with groups of indistinguishable individuals, which has the potential to prevent tracing back a query result to a particular individual in a group.…”