Symbolic data analysis is a fairly recent technique for the analysis of large and complex datasets based on summarising the data into a number of "symbols" prior to analysis. Inference is then based on the analysis of the data at the symbol level (modelling symbols, predicting symbols etc). In principle this idea works, however it would be more advantageous and natural to fit models at the level of the underlying data, rather than the symbol. Here we develop a new class of models for the analysis of symbolic data that fit directly to the data underlying the symbol, allowing for a more intuitive and flexible approach to analysis using this technique.


Scott Sisson

Research Area

The University of New South Wales


Fri, 21/10/2016 - 4:00pm


RC-M032, Red Centre, UNSW