54 research outputs found
Uniform test of algorithmic randomness over a general space
The algorithmic theory of randomness is well developed when the underlying
space is the set of finite or infinite sequences and the underlying probability
distribution is the uniform distribution or a computable distribution. These
restrictions seem artificial. Some progress has been made to extend the theory
to arbitrary Bernoulli distributions (by Martin-Loef), and to arbitrary
distributions (by Levin). We recall the main ideas and problems of Levin's
theory, and report further progress in the same framework.
- We allow non-compact spaces (like the space of continuous functions,
underlying the Brownian motion).
- The uniform test (deficiency of randomness) d_P(x) (depending both on the
outcome x and the measure P should be defined in a general and natural way.
- We see which of the old results survive: existence of universal tests,
conservation of randomness, expression of tests in terms of description
complexity, existence of a universal measure, expression of mutual information
as "deficiency of independence.
- The negative of the new randomness test is shown to be a generalization of
complexity in continuous spaces; we show that the addition theorem survives.
The paper's main contribution is introducing an appropriate framework for
studying these questions and related ones (like statistics for a general family
of distributions).Comment: 40 pages. Journal reference and a slight correction in the proof of
Theorem 7 adde
A constructive version of Birkhoff's ergodic theorem for Martin-L\"of random points
A theorem of Ku\v{c}era states that given a Martin-L\"of random infinite
binary sequence {\omega} and an effectively open set A of measure less than 1,
some tail of {\omega} is not in A. We first prove several results in the same
spirit and generalize them via an effective version of a weak form of
Birkhoff's ergodic theorem. We then use this result to get a stronger form of
it, namely a very general effective version of Birkhoff's ergodic theorem,
which improves all the results previously obtained in this direction, in
particular those of V'Yugin, Nandakumar and Hoyrup, Rojas.Comment: Improved version of the CiE'10 paper, with the strong form of
Birkhoff's ergodic theorem for random point
Random semicomputable reals revisited
The aim of this expository paper is to present a nice series of results,
obtained in the papers of Chaitin (1976), Solovay (1975), Calude et al. (1998),
Kucera and Slaman (2001). This joint effort led to a full characterization of
lower semicomputable random reals, both as those that can be expressed as a
"Chaitin Omega" and those that are maximal for the Solovay reducibility. The
original proofs were somewhat involved; in this paper, we present these results
in an elementary way, in particular requiring only basic knowledge of
algorithmic randomness. We add also several simple observations relating lower
semicomputable random reals and busy beaver functions.Comment: 15 page
Asymmetry of the Kolmogorov complexity of online predicting odd and even bits
Symmetry of information states that .
We show that a similar relation for online Kolmogorov complexity does not hold.
Let the even (online Kolmogorov) complexity of an n-bitstring
be the length of a shortest program that computes on input ,
computes on input , etc; and similar for odd complexity. We
show that for all n there exist an n-bit x such that both odd and even
complexity are almost as large as the Kolmogorov complexity of the whole
string. Moreover, flipping odd and even bits to obtain a sequence
, decreases the sum of odd and even complexity to .Comment: 20 pages, 7 figure
The Dimensions of Individual Strings and Sequences
A constructive version of Hausdorff dimension is developed using constructive
supergales, which are betting strategies that generalize the constructive
supermartingales used in the theory of individual random sequences. This
constructive dimension is used to assign every individual (infinite, binary)
sequence S a dimension, which is a real number dim(S) in the interval [0,1].
Sequences that are random (in the sense of Martin-Lof) have dimension 1, while
sequences that are decidable, \Sigma^0_1, or \Pi^0_1 have dimension 0. It is
shown that for every \Delta^0_2-computable real number \alpha in [0,1] there is
a \Delta^0_2 sequence S such that \dim(S) = \alpha.
A discrete version of constructive dimension is also developed using
termgales, which are supergale-like functions that bet on the terminations of
(finite, binary) strings as well as on their successive bits. This discrete
dimension is used to assign each individual string w a dimension, which is a
nonnegative real number dim(w). The dimension of a sequence is shown to be the
limit infimum of the dimensions of its prefixes.
The Kolmogorov complexity of a string is proven to be the product of its
length and its dimension. This gives a new characterization of algorithmic
information and a new proof of Mayordomo's recent theorem stating that the
dimension of a sequence is the limit infimum of the average Kolmogorov
complexity of its first n bits.
Every sequence that is random relative to any computable sequence of
coin-toss biases that converge to a real number \beta in (0,1) is shown to have
dimension \H(\beta), the binary entropy of \beta.Comment: 31 page
Relating and contrasting plain and prefix Kolmogorov complexity
In [3] a short proof is given that some strings have maximal plain Kolmogorov
complexity but not maximal prefix-free complexity. The proof uses Levin's
symmetry of information, Levin's formula relating plain and prefix complexity
and Gacs' theorem that complexity of complexity given the string can be high.
We argue that the proof technique and results mentioned above are useful to
simplify existing proofs and to solve open questions.
We present a short proof of Solovay's result [21] relating plain and prefix
complexity: and , (here denotes , etc.).
We show that there exist such that is infinite and is
finite, i.e. the infinitely often C-trivial reals are not the same as the
infinitely often K-trivial reals (i.e. [1,Question 1]).
Solovay showed that for infinitely many we have
and , (here
denotes the length of and , etc.). We show that this
result holds for prefixes of some 2-random sequences.
Finally, we generalize our proof technique and show that no monotone relation
exists between expectation and probability bounded randomness deficiency (i.e.
[6, Question 1]).Comment: 20 pages, 1 figur
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