STATICA
Statistical Methods for ICT Applications
Research
STATICA's
research interests centre around issues of applying Bayesian methods of
statistical inference to a wide variety of applications, motivated by
the "data explosion" of the last few decades.
Below are brief descriptions of the research projects that are
currently being undertaken by STATICA researchers.
Source separation for multi-spectral
images:
STATICA post-doctoral researcher Ji Won Yoon is working with Simon
Wilson and colleagues in information science, Drs. Ercan Kuruoğlu
and Emanuele Salerno of CNR Pisa,
on statistical methods for source separation. It is motivated by
the problem of source separation for the cosmic
microwave background (CMB). This is a very exciting time for
the study of this phenomenon that is believed to be a relic of the
early state of the Universe. A new satellite, Planck,
was launched this year that will observe CMB in unprecedented detail
and is already starting to produce data. The statistical problem
here is that CMB is not the only source of microwaves in the sky, and
so we must "separate" out the contribution of CMB from all of these
other sources. Our particular interest in this is how to quantify
the uncertainty in our reconstruction of the CMB that arises because of
this complication. The project was given initial funcding by the
now-concluded EU-funded project MUSCLE.
Estimating the number of species of
different taxa:
Simon Wilson is working with STATICA post-doctoral researcher Brett Houlding
and Dr.
Mark Costello of the Leigh
Marine Laboratory, University
of Auckland on estimating the total numbers of marine species of
different taxa. Currently this is based on data from the dates of
first reporting of different species, starting with the initial work of
Linnaeus.
This question is important in discussion of species extinction rates
and biodiversity. We form part of a global team, including
researchers in Canada, France, the United States and Australia, that is
evaluating the many different methods of estimating species numbers.
Implementing Bayesian inverse
regression using variational Bayes:
An important aspect of STATICA research, that cuts across much of its
work, is fast approximations to posterior distributions that arise in
the implementation of Bayesian statistical methods. One approach
is the method of variational Bayes, a functional approximation.
Work with graduate student Richa Vatsa is trying to improve the
accuracy of variational Bayes when it is used in inverse regression
problems. The motivating example here is in reconstructing
ancient climates from so-called proxy data, such as pollen deposited in
lake sediment. A regression model relates the amount of different
species that are deposited into lakes as a function of climate.
This is fitted using modern data on both pollen adundances and the
climate where it is deposited. On observing ancient pollen, we
invert this regression model to infer climate. Good climate
reconstructions depend on good modelling of the relationships and (most
importantly) dependencies between different pollen types and climate;
this leads to a very complicated inference procedure that VB offers
some advantages to solving. This project was initially funded by Science Foundation Ireland under its
Research Frontiers Programme.
Fast Bayesian updating for dynamic
models with Laplace approximations
Another important set of statistical problems involve dynamic data that is arriving in time, perhaps quite quickly, such as video or audio streams, and some analysis is needed in ''real time''. One approach is to dynamically update estimates and predictions as new data arrive. STATICA postgraduate Arnab Bhattacharya is looking at new ways to do Bayesian inference dynamically through Laplace approximations, and their recent extensions due to Rue et al. This project was initially funded by Science Foundation Ireland under the Centre
for Telecommunications Value-Chain Research.