575 research outputs found
Integrity Constraints Revisited: From Exact to Approximate Implication
Integrity constraints such as functional dependencies (FD), and multi-valued
dependencies (MVD) are fundamental in database schema design. Likewise,
probabilistic conditional independences (CI) are crucial for reasoning about
multivariate probability distributions. The implication problem studies whether
a set of constraints (antecedents) implies another constraint (consequent), and
has been investigated in both the database and the AI literature, under the
assumption that all constraints hold exactly. However, many applications today
consider constraints that hold only approximately. In this paper we define an
approximate implication as a linear inequality between the degree of
satisfaction of the antecedents and consequent, and we study the relaxation
problem: when does an exact implication relax to an approximate implication? We
use information theory to define the degree of satisfaction, and prove several
results. First, we show that any implication from a set of data dependencies
(MVDs+FDs) can be relaxed to a simple linear inequality with a factor at most
quadratic in the number of variables; when the consequent is an FD, the factor
can be reduced to 1. Second, we prove that there exists an implication between
CIs that does not admit any relaxation; however, we prove that every
implication between CIs relaxes "in the limit". Finally, we show that the
implication problem for differential constraints in market basket analysis also
admits a relaxation with a factor equal to 1. Our results recover, and
sometimes extend, several previously known results about the implication
problem: implication of MVDs can be checked by considering only 2-tuple
relations, and the implication of differential constraints for frequent item
sets can be checked by considering only databases containing a single
transaction
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On defining partition entropy by inequalities
Partition entropy is the numerical metric of uncertainty within
a partition of a finite set, while conditional entropy measures the degree of
difficulty in predicting a decision partition when a condition partition is
provided. Since two direct methods exist for defining conditional entropy
based on its partition entropy, the inequality postulates of monotonicity,
which conditional entropy satisfies, are actually additional constraints on
its entropy. Thus, in this paper partition entropy is defined as a function
of probability distribution, satisfying all the inequalities of not only partition
entropy itself but also its conditional counterpart. These inequality
postulates formalize the intuitive understandings of uncertainty contained
in partitions of finite sets.We study the relationships between these inequalities,
and reduce the redundancies among them. According to two different
definitions of conditional entropy from its partition entropy, the convenient
and unified checking conditions for any partition entropy are presented, respectively.
These properties generalize and illuminate the common nature
of all partition entropies
Integrity Constraints Revisited: From Exact to Approximate Implication
Integrity constraints such as functional dependencies (FD), and multi-valued dependencies (MVD) are fundamental in database schema design. Likewise, probabilistic conditional independences (CI) are crucial for reasoning about multivariate probability distributions. The implication problem studies whether a set of constraints (antecedents) implies another constraint (consequent), and has been investigated in both the database and the AI literature, under the assumption that all constraints hold exactly. However, many applications today consider constraints that hold only approximately. In this paper we define an approximate implication as a linear inequality between the degree of satisfaction of the antecedents and consequent, and we study the relaxation problem: when does an exact implication relax to an approximate implication? We use information theory to define the degree of satisfaction, and prove several results. First, we show that any implication from a set of data dependencies (MVDs+FDs) can be relaxed to a simple linear inequality with a factor at most quadratic in the number of variables; when the consequent is an FD, the factor can be reduced to 1. Second, we prove that there exists an implication between CIs that does not admit any relaxation; however, we prove that every implication between CIs relaxes "in the limit". Finally, we show that the implication problem for differential constraints in market basket analysis also admits a relaxation with a factor equal to 1. Our results recover, and sometimes extend, several previously known results about the implication problem: implication of MVDs can be checked by considering only 2-tuple relations, and the implication of differential constraints for frequent item sets can be checked by considering only databases containing a single transaction
Fredkin Gates for Finite-valued Reversible and Conservative Logics
The basic principles and results of Conservative Logic introduced by Fredkin
and Toffoli on the basis of a seminal paper of Landauer are extended to
d-valued logics, with a special attention to three-valued logics. Different
approaches to d-valued logics are examined in order to determine some possible
universal sets of logic primitives. In particular, we consider the typical
connectives of Lukasiewicz and Godel logics, as well as Chang's MV-algebras. As
a result, some possible three-valued and d-valued universal gates are described
which realize a functionally complete set of fundamental connectives.Comment: 57 pages, 10 figures, 16 tables, 2 diagram
Generalized probabilities in statistical theories
In this review article we present different formal frameworks for the
description of generalized probabilities in statistical theories. We discuss
the particular cases of probabilities appearing in classical and quantum
mechanics, possible generalizations of the approaches of A. N. Kolmogorov and
R. T. Cox to non-commutative models, and the approach to generalized
probabilities based on convex sets
T-entropy and Variational Principle for the spectral radius of transfer and weighted shift operators
The paper deals with the variational principles for evaluation of the
spectral radii of transfer and weighted shift operators associated with a
dynamical system. These variational principles have been the matter of numerous
investigations and the principal results have been achieved in the situation
when the dynamical system is either reversible or it is a topological Markov
chain. As the main summands these principles contain the integrals over
invariant measures and the Kolmogorov--Sinai entropy. In the article we derive
the Variational Principle for an arbitrary dynamical system. It gives the
explicit description of the Legendre dual object to the spectral potential. It
is shown that in general this principle contains not the Kolmogorov--Sinai
entropy but a new invariant of entropy type -- the t-entropy.Comment: 51 pages, v.2: editorial correction
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