Abstract:
Stochastic Complexity (SC) was introduced by Rissanen in 1978 and since then various forms of it have been derived. According to the Minimum Description Length principle, SC is defined in the context of transmitting the existing data to a decoder. The “encoding” is performed by using mathematical models that belong to a predefined class, and the model which leads to the shortest code length is deemed to be the most suitable for describing the data. In this talk, we discuss two recent applications of SC. The first one is a difficult biological problem which concerns identification of the “correct” evolutionary tree. The results were obtained by the author together with Cenanning Li and Dr. Peter J. Waddell. The second application is a joint work with Said Maanan and Prof. Bogdan Dumitrescu, in which we employ a novel SC-criterion for selecting the order of vector autoregressive processes. We pay a special attention to models for which the inverse spectral density matrix (ISDM) has a specific sparsity pattern. The interest on these models is motivated by the relationship between sparse structure of ISDM and the problem of inferring the conditional independence graph for multivariate time series.
Bio:
CD Giurcaneanu received the Ph.D. degree (with commendation) from Tampere University of Technology (TUT), Finland, in 2001. From 1993 to 1997, he was a Junior Assistant at "Politehnica" University of Bucharest. In 1997 he joined TUT where he spent more than 14 years as a Researcher, Senior Researcher and Academy Research Fellow in the Department of Signal Processing. From January 2012 to June 2012 he was with Helsinki Institute for Information Technology (HIIT), and in July 2012 he joined the Department of Statistics, University of Auckland, where he is currently a Senior Lecturer. His research is mainly focused on stochastic complexity and its applications.