M.Sc. Panu Luosto will defend his doctoral thesis Normalized maximum likelihood methods for clustering and density estimation on Saturday 14th of September 2013 at 10 o'clock in the University of Helsinki Main Building, Unioninkatu 34, Auditorium XIV (old part), 3rd floor. His opponent is Docent, Academy researcher Jaakko Peltonen (Aalto University) and custos Professor Jyrki Kivinen (University of Helsinki). The defense will be held in Finnish.

Normalized maximum likelihood methods for clustering and density estimation

The normalized maximum likelihood (NML) distribution has an important position in minimum description length based modelling. Given a set of possible models, the corresponding NML distribution enables optimal encoding according to the worst-case criterion. However, many model classes of practical interest do not have an NML distribution. This thesis introduces solutions for a selection of such cases, including for example one-dimensional normal, uniform and exponential model classes with unrestricted parameters. The new code length functions are based on minimal assumptions about the data, because an approach that would be completely free of any assumptions is not possible in these cases. We also use the new techniques in clustering, as well as in density and entropy estimation applications.

Availability of the dissertation

An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-952-10-9180-3.

Printed copies are available on request from Panu Luosto: panu.luosto@cs.helsinki.fi.