1,125 research outputs found
On impossibility of sequential algorithmic forecasting
The problem of prediction future event given an individual
sequence of past events is considered. Predictions are given
in form of real numbers which are computed by some algorithm
using initial fragments
of an individual binary sequence
and can be interpreted as probabilities of the event
given this fragment.
According to Dawid\u27s {it prequential framework}
%we do not consider
%numbers as conditional probabilities generating by some
%overall probability distribution on the set of all possible events.
we consider partial forecasting algorithms which are
defined on all initial fragments of and can
be undefined outside the given sequence of outcomes.
We show that even for this large class of forecasting algorithms
combining outcomes of coin-tossing and transducer algorithm
it is possible to efficiently generate with probability close
to one sequences
for which any partial forecasting algorithm is failed by the
method of verifying called {it calibration}
Estimating the Algorithmic Complexity of Stock Markets
Randomness and regularities in Finance are usually treated in probabilistic
terms. In this paper, we develop a completely different approach in using a
non-probabilistic framework based on the algorithmic information theory
initially developed by Kolmogorov (1965). We present some elements of this
theory and show why it is particularly relevant to Finance, and potentially to
other sub-fields of Economics as well. We develop a generic method to estimate
the Kolmogorov complexity of numeric series. This approach is based on an
iterative "regularity erasing procedure" implemented to use lossless
compression algorithms on financial data. Examples are provided with both
simulated and real-world financial time series. The contributions of this
article are twofold. The first one is methodological : we show that some
structural regularities, invisible with classical statistical tests, can be
detected by this algorithmic method. The second one consists in illustrations
on the daily Dow-Jones Index suggesting that beyond several well-known
regularities, hidden structure may in this index remain to be identified
06051 Abstracts Collection -- Kolmogorov Complexity and Applications
From 29.01.06 to 03.02.06, the Dagstuhl Seminar 06051 ``Kolmogorov Complexity and Applications\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl. During the seminar, several participants presented
their current research, and ongoing work and open problems were
discussed. Abstracts of the presentations given during the seminar
as well as abstracts of seminar results and ideas are put together
in this paper. The first section describes the seminar topics and
goals in general. Links to extended abstracts or full papers are
provided, if available
MODELING OF THE PROSPECTS FOR SUSTAINABLE DEVELOPMENT OF AGRICULTURAL TERRITORIES BY THE BAYESIAN NETWORKS
The article gives information on the problem of sustainable development of the agricultural territories, as to provide the effective path to follow through in the future. The aim of the research is to provide a scientific basis for the need of using the modeling with the help of neural network technologies and to build a Bayesian belief network to make a decision on the sustainable development of the village council of Gladkovich and Hoteshiv in the future. It is carried out the estimation of factors and conditions influencing the sustainable development of the studied area. The results of the implementation of the Bayesian network with the help of Netica software for deciding on the sustainable development of village councils in the future based on the questionnaire data during the period of decentralization and association of territorial communities are outlined.Straipsnyje pateikiama informacija apie kaimo vietovių tvarios plėtros problemą, siekiant užtikrinti veiksmingą ateities situacijos sprendimą. Tyrimo tikslas – pateikti mokslinį modeliavimo pagrindimą naudojant neuroninių tinklų technologijas ir sukuriant Bajeso pasitikėjimo tinklus, siekiant priimti sprendimą dėl tvaraus vystymosi Gladkovich ir Hotešivo kaimo vietovių ateities. Atliktas veiksnių ir sąlygų, turinčių įtakos darnaus vystymosi sričiai, įvertinimas. Remiantis apklausos duomenimis decentralizacijos ir teritorinių bendruomenių asociacijos laikotarpiu, apibūdinti Bajeso tinklo įgyvendinimo rezultatai naudojant „Netica“ programinę įrang
On Universal Prediction and Bayesian Confirmation
The Bayesian framework is a well-studied and successful framework for
inductive reasoning, which includes hypothesis testing and confirmation,
parameter estimation, sequence prediction, classification, and regression. But
standard statistical guidelines for choosing the model class and prior are not
always available or fail, in particular in complex situations. Solomonoff
completed the Bayesian framework by providing a rigorous, unique, formal, and
universal choice for the model class and the prior. We discuss in breadth how
and in which sense universal (non-i.i.d.) sequence prediction solves various
(philosophical) problems of traditional Bayesian sequence prediction. We show
that Solomonoff's model possesses many desirable properties: Strong total and
weak instantaneous bounds, and in contrast to most classical continuous prior
densities has no zero p(oste)rior problem, i.e. can confirm universal
hypotheses, is reparametrization and regrouping invariant, and avoids the
old-evidence and updating problem. It even performs well (actually better) in
non-computable environments.Comment: 24 page
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
Related information at this http://axtnt2.phys.uniroma1.i
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