School of Electronic Engineering and Computer Science

Information Retrieval Models for Probabilistic Data Analysis

Supervisor: Dr Thomas Roelleke

Research group(s): Risk & Information Management

Information Retrieval (IR) relies on probability theory. Retrieval models deliver a relevance-based ranking of retrieved objects, and many decades of research have shown that the independence assumption (often applied in probability theory) leads to sub-optimal retrieval quality. IR models incorporate DEPENDENCE ASSUMPTIONS, and therefore achieve good retrieval quality. The aim of this project is to transfer IR models to the general world of probability theory. The hypothesis is that modelling the dependence in probabilistic models (e.g. for health and law) can significantly improve the quality of models used for data analysis.