62 research outputs found
An Algebraic Theory for Data Linkage
There are countless sources of data available to governments, companies, and citizens, which can be combined for good or evil. We analyse the concepts of combining data from common sources and linking data from different sources. We model the data and its information content to be found in a single source by an ordered partial monoid, and the transfer of information between sources by different types of morphisms. To capture the linkage between a family of sources, we use a form of Grothendieck construction to create an ordered partial monoid that brings together the global data of the family in a single structure. We apply our approach to database theory and axiomatic structures in approximate reasoning. Thus, ordered partial monoids provide a foundation for the algebraic study for information gathering in its most primitive form
A Probabilistic Framework for Security Scenarios with Dependent Actions
This work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform probabilistic evaluation of attack–defense scenarios involving dependent actions. To improve the efficiency of our computations, we make use of inference algorithms from Bayesian networks and encoding techniques from constraint reasoning. We discuss the algebraic theory underlying our framework and point out several generalizations which are possible thanks to the use of semiring theory
A frequentist framework of inductive reasoning
Reacting against the limitation of statistics to decision procedures, R. A.
Fisher proposed for inductive reasoning the use of the fiducial distribution, a
parameter-space distribution of epistemological probability transferred
directly from limiting relative frequencies rather than computed according to
the Bayes update rule. The proposal is developed as follows using the
confidence measure of a scalar parameter of interest. (With the restriction to
one-dimensional parameter space, a confidence measure is essentially a fiducial
probability distribution free of complications involving ancillary statistics.)
A betting game establishes a sense in which confidence measures are the only
reliable inferential probability distributions. The equality between the
probabilities encoded in a confidence measure and the coverage rates of the
corresponding confidence intervals ensures that the measure's rule for
assigning confidence levels to hypotheses is uniquely minimax in the game.
Although a confidence measure can be computed without any prior distribution,
previous knowledge can be incorporated into confidence-based reasoning. To
adjust a p-value or confidence interval for prior information, the confidence
measure from the observed data can be combined with one or more independent
confidence measures representing previous agent opinion. (The former confidence
measure may correspond to a posterior distribution with frequentist matching of
coverage probabilities.) The representation of subjective knowledge in terms of
confidence measures rather than prior probability distributions preserves
approximate frequentist validity.Comment: major revisio
Propositional information systems
Resolution is an often used method for deduction in propositional logic. Here a proper organization of deduction is proposed which avoids redundant computations. It is based on a generic framework of decompositions and local computations as introduced by Shenoy and Shafer. The system contains the two basic operations with information, namely marginalization (or projection) and combination; the latter being an idempotent operation in the present case. The theory permits the conception of an architecture of distributed computing. As an important application assumption-based reasoning is discusse
Reliability and diagnostic of modular systems
Reliability and diagnostic are in general two problems discussed separately. Yet the two problems are in fact closely related to each other. Here, this relation is considered in the simple case of modular systems. We show, how the computation of reliability and diagnostic can efficiently be done within the same Bayesian network induced by the modularity of the structure function of the system
Importance Measures from Reliability Theory for Probabilistic Assumption-Based Reasoning
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