Algorithmic Statistics
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Beschreibung
Whereas Kolmogorov complexity is the accepted absolute measure of
information content of an individual finite object, a similarly
absolute notion is needed for the relation between an individual data
sample and an individual model summarizing the information in the data
---for example, a finite set (or probability distribution) where the
data sample typically came from. The statistical theory based on such
relations between individual objects can be called algorithmic
statistics, in contrast to classical statistical theory that deals
with relations between probabilistic ensembles.
'Algorithmic Statistics' develops the algorithmic theory of
statistics, sufficient statistics, and minimal sufficient statistics.
This theory is based on two-part codes consisting of the code for the
statistic (the model summarizing the regularity, the meaningful
information, in the data) and the model-to-data code. In contrast to
the situation in probabilistic statistical theory, the algorithmic
relation of (minimal) sufficiency is an absolute relation between the
individual model and the individual data sample. The book
distinguishes implicit and explicit descriptions of the models and
gives characterizations of algorithmic (Kolmogorov) minimal sufficient
statistic for all data samples for both description modes--in the
explicit mode under some constraints. It also strengthens and
elaborates upon earlier results on the ``Kolmogorov structure
function'' and ``absolutely non-stochastic objects''--those rare
objects for which the simplest models that summarize their relevant
information (minimal sufficient statistics) are at least as complex as
the objects themselves.
A major scientific presentation, by a leading international
authority, on the relationship between probabilistic concepts and
algorithmic concepts for computational purposes. Vitanyi provides
detailed presentation of new ideas in probability theory and data
analysis, and how the two fundamental topics are related by an
algorithmic theoretical framework.
Essential book for researchers and scientists in CS, AI, and
computational statistics who use fundamental algorithms in their work.
Autor*in
Paul M.B. Vitányi