79,849 research outputs found
Automated Termination Proofs for Logic Programs by Term Rewriting
There are two kinds of approaches for termination analysis of logic programs:
"transformational" and "direct" ones. Direct approaches prove termination
directly on the basis of the logic program. Transformational approaches
transform a logic program into a term rewrite system (TRS) and then analyze
termination of the resulting TRS instead. Thus, transformational approaches
make all methods previously developed for TRSs available for logic programs as
well. However, the applicability of most existing transformations is quite
restricted, as they can only be used for certain subclasses of logic programs.
(Most of them are restricted to well-moded programs.) In this paper we improve
these transformations such that they become applicable for any definite logic
program. To simulate the behavior of logic programs by TRSs, we slightly modify
the notion of rewriting by permitting infinite terms. We show that our
transformation results in TRSs which are indeed suitable for automated
termination analysis. In contrast to most other methods for termination of
logic programs, our technique is also sound for logic programming without occur
check, which is typically used in practice. We implemented our approach in the
termination prover AProVE and successfully evaluated it on a large collection
of examples.Comment: 49 page
Signal processing with Levy information
Levy processes, which have stationary independent increments, are ideal for
modelling the various types of noise that can arise in communication channels.
If a Levy process admits exponential moments, then there exists a parametric
family of measure changes called Esscher transformations. If the parameter is
replaced with an independent random variable, the true value of which
represents a "message", then under the transformed measure the original Levy
process takes on the character of an "information process". In this paper we
develop a theory of such Levy information processes. The underlying Levy
process, which we call the fiducial process, represents the "noise type". Each
such noise type is capable of carrying a message of a certain specification. A
number of examples are worked out in detail, including information processes of
the Brownian, Poisson, gamma, variance gamma, negative binomial, inverse
Gaussian, and normal inverse Gaussian type. Although in general there is no
additive decomposition of information into signal and noise, one is led
nevertheless for each noise type to a well-defined scheme for signal detection
and enhancement relevant to a variety of practical situations.Comment: 27 pages. Version to appear in: Proc. R. Soc. London
Phase Transitions in Nonlinear Filtering
It has been established under very general conditions that the ergodic
properties of Markov processes are inherited by their conditional distributions
given partial information. While the existing theory provides a rather complete
picture of classical filtering models, many infinite-dimensional problems are
outside its scope. Far from being a technical issue, the infinite-dimensional
setting gives rise to surprising phenomena and new questions in filtering
theory. The aim of this paper is to discuss some elementary examples,
conjectures, and general theory that arise in this setting, and to highlight
connections with problems in statistical mechanics and ergodic theory. In
particular, we exhibit a simple example of a uniformly ergodic model in which
ergodicity of the filter undergoes a phase transition, and we develop some
qualitative understanding as to when such phenomena can and cannot occur. We
also discuss closely related problems in the setting of conditional Markov
random fields.Comment: 51 page
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