1,866 research outputs found
Symbolic stochastic dynamical systems viewed as binary N-step Markov chains
A theory of systems with long-range correlations based on the consideration
of binary N-step Markov chains is developed. In the model, the conditional
probability that the i-th symbol in the chain equals zero (or unity) is a
linear function of the number of unities among the preceding N symbols. The
correlation and distribution functions as well as the variance of number of
symbols in the words of arbitrary length L are obtained analytically and
numerically. A self-similarity of the studied stochastic process is revealed
and the similarity group transformation of the chain parameters is presented.
The diffusion Fokker-Planck equation governing the distribution function of the
L-words is explored. If the persistent correlations are not extremely strong,
the distribution function is shown to be the Gaussian with the variance being
nonlinearly dependent on L. The applicability of the developed theory to the
coarse-grained written and DNA texts is discussed.Comment: 14 pages, 13 figure
Competition between Two Kinds of Correlations in Literary Texts
A theory of additive Markov chains with long-range memory is used for
description of correlation properties of coarse-grained literary texts. The
complex structure of the correlations in texts is revealed. Antipersistent
correlations at small distances, L 300 define
this nontrivial structure. For some concrete examples of literary texts, the
memory functions are obtained and their power-law behavior at long distances is
disclosed. This property is shown to be a cause of self-similarity of texts
with respect to the decimation procedure.Comment: 7 pages, 7 figures, Submitted to Physica
Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"
This special issue collects contributions from the participants of the
"Information in Dynamical Systems and Complex Systems" workshop, which cover a
wide range of important problems and new approaches that lie in the
intersection of information theory and dynamical systems. The contributions
include theoretical characterization and understanding of the different types
of information flow and causality in general stochastic processes, inference
and identification of coupling structure and parameters of system dynamics,
rigorous coarse-grain modeling of network dynamical systems, and exact
statistical testing of fundamental information-theoretic quantities such as the
mutual information. The collective efforts reported herein reflect a modern
perspective of the intimate connection between dynamical systems and
information flow, leading to the promise of better understanding and modeling
of natural complex systems and better/optimal design of engineering systems
Consistency of maximum likelihood estimation for some dynamical systems
We consider the asymptotic consistency of maximum likelihood parameter
estimation for dynamical systems observed with noise. Under suitable conditions
on the dynamical systems and the observations, we show that maximum likelihood
parameter estimation is consistent. Our proof involves ideas from both
information theory and dynamical systems. Furthermore, we show how some
well-studied properties of dynamical systems imply the general statistical
properties related to maximum likelihood estimation. Finally, we exhibit
classical families of dynamical systems for which maximum likelihood estimation
is consistent. Examples include shifts of finite type with Gibbs measures and
Axiom A attractors with SRB measures.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1259 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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