1,191 research outputs found
Game interpretation of Kolmogorov complexity
The Kolmogorov complexity function K can be relativized using any oracle A,
and most properties of K remain true for relativized versions. In section 1 we
provide an explanation for this observation by giving a game-theoretic
interpretation and showing that all "natural" properties are either true for
all sufficiently powerful oracles or false for all sufficiently powerful
oracles. This result is a simple consequence of Martin's determinacy theorem,
but its proof is instructive: it shows how one can prove statements about
Kolmogorov complexity by constructing a special game and a winning strategy in
this game. This technique is illustrated by several examples (total conditional
complexity, bijection complexity, randomness extraction, contrasting plain and
prefix complexities).Comment: 11 pages. Presented in 2009 at the conference on randomness in
Madison
Non-Singular Bouncing Universes in Loop Quantum Cosmology
Non-perturbative quantum geometric effects in Loop Quantum Cosmology predict
a modification to the Friedmann equation at high energies. The
quadratic term is negative definite and can lead to generic bounces when the
matter energy density becomes equal to a critical value of the order of the
Planck density. The non-singular bounce is achieved for arbitrary matter
without violation of positive energy conditions. By performing a qualitative
analysis we explore the nature of the bounce for inflationary and Cyclic model
potentials. For the former we show that inflationary trajectories are
attractors of the dynamics after the bounce implying that inflation can be
harmoniously embedded in LQC. For the latter difficulties associated with
singularities in cyclic models can be overcome. We show that non-singular
cyclic models can be constructed with a small variation in the original Cyclic
model potential by making it slightly positive in the regime where scalar field
is negative.Comment: Minor changes and one figure added to improve presentation.
References added. To appear in Physical Review
Algorithmic statistics revisited
The mission of statistics is to provide adequate statistical hypotheses
(models) for observed data. But what is an "adequate" model? To answer this
question, one needs to use the notions of algorithmic information theory. It
turns out that for every data string one can naturally define
"stochasticity profile", a curve that represents a trade-off between complexity
of a model and its adequacy. This curve has four different equivalent
definitions in terms of (1)~randomness deficiency, (2)~minimal description
length, (3)~position in the lists of simple strings and (4)~Kolmogorov
complexity with decompression time bounded by busy beaver function. We present
a survey of the corresponding definitions and results relating them to each
other
Algorithmic statistics: forty years later
Algorithmic statistics has two different (and almost orthogonal) motivations.
From the philosophical point of view, it tries to formalize how the statistics
works and why some statistical models are better than others. After this notion
of a "good model" is introduced, a natural question arises: it is possible that
for some piece of data there is no good model? If yes, how often these bad
("non-stochastic") data appear "in real life"?
Another, more technical motivation comes from algorithmic information theory.
In this theory a notion of complexity of a finite object (=amount of
information in this object) is introduced; it assigns to every object some
number, called its algorithmic complexity (or Kolmogorov complexity).
Algorithmic statistic provides a more fine-grained classification: for each
finite object some curve is defined that characterizes its behavior. It turns
out that several different definitions give (approximately) the same curve.
In this survey we try to provide an exposition of the main results in the
field (including full proofs for the most important ones), as well as some
historical comments. We assume that the reader is familiar with the main
notions of algorithmic information (Kolmogorov complexity) theory.Comment: Missing proofs adde
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