### Computational Information Technology

Computational information technology deals with problems where
efficient computational methods are combined with the problems of
information processing. During recent years, computational approach
in information processing has gained increasing importance, with
progress in fields like **computational intelligence**
and **data mining**.
Computational intelligence comprises methods such as neural
networks, probabilistic methods, fuzzy systems and evolutionary
computing, where iterative numerical methods are used for solving
complex propblems, involving large amounts of measured data and
only uncertain prior knowledge about the solutions or phenomena to
be modelled. A central element is inference in novel situations,
based on observed data, that is, inductive learning systems.
Similar capabilities for knowledge discovery are needed in data
mining, where useful pieces of information are sought from large,
possibly unorganized, databases.

In the laboratory the research in computational information
technology is focused in neural computing, Bayesian methods, and
pattern recognition and modeling applications. A good example of
fields in information processing, in which computational methods
have large impact, is the Bayesian approach, where the estimation
of a specific set of model parameters is replaced by constructing
the propbability distribution of the model parameters and
integrating over it during the use of the model. This integration
requires efficient numerical methods, such as Markov chain Monte
Carlo methods. The numerical solution also makes it posible to
avoid some standard assumptions (such as linearity and/or
gaussianity), that may be bad in practice.

In the following chapters we give short descriptions of the
research projects in the computational information technology
group.

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