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Book reviews
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45707/1/11336_2005_Article_BF02288937.pd
ARTIFICIAL INTELLIGENCE DIALECTS OF THE BAYESIAN BELIEF REVISION LANGUAGE
Rule-based expert systems must deal with uncertain data,
subjective expert opinions, and inaccurate decision rules. Computer scientists
and psychologists have proposed and implemented a number of belief languages widely used in applied systems, and their normative validity is clearly an important question, both on practical as well on theoretical grounds. Several well-know belief languages are reviewed, and both previous work and new insights into their Bayesian interpretations are presented. In
particular, the authors focus on three alternative belief-update models the
certainty factors calculus, Dempster-Shafer simple support functions, and
the descriptive contrast/inertia model. Important "dialectsâ of these
languages are shown to be isomorphic to each other and to a special case of
Bayesian inference. Parts of this analysis were carried out by other authors; these results were extended and consolidated using an analytic technique designed to study the kinship of belief languages in general.Information Systems Working Papers Serie
Linguistic probability theory
In recent years probabilistic knowledge-based systems such as Bayesian networks and influence diagrams have come to the fore as a means of representing and reasoning about complex real-world situations. Although some of the
probabilities used in these models may be obtained statistically, where this is
impossible or simply inconvenient, modellers rely on expert knowledge. Experts, however, typically find it difficult to specify exact probabilities and conventional representations cannot reflect any uncertainty they may have. In
this way, the use of conventional point probabilities can damage the accuracy,
robustness and interpretability of acquired models. With these concerns in
mind, psychometric researchers have demonstrated that fuzzy numbers are
good candidates for representing the inherent vagueness of probability estimates, and the fuzzy community has responded with two distinct theories of
fuzzy probabilities.This thesis, however, identifies formal and presentational problems with these
theories which render them unable to represent even very simple scenarios.
This analysis leads to the development of a novel and intuitively appealing
alternative - a
theory of linguistic probabilities patterned after the standard Kolmogorov axioms of probability theory. Since fuzzy numbers lack algebraic
inverses, the resulting theory is weaker than, but generalises its classical counterpart. Nevertheless, it is demonstrated that analogues for classical probabilistic concepts such as conditional probability and random variables can be
constructed. In the classical theory, representation theorems mean that most of
the time the distinction between mass/density distributions and probability
measures can be ignored. Similar results are proven for linguistic probabiliities.From these results it is shown that directed acyclic graphs annotated with linguistic probabilities (under certain identified conditions) represent systems of
linguistic random variables. It is then demonstrated these linguistic Bayesian
networks can utilise adapted best-of-breed Bayesian network algorithms (junction tree based inference and Bayes' ball irrelevancy calculation). These algorithms are implemented in ARBOR, an interactive design, editing and querying
tool for linguistic Bayesian networks.To explore the applications of these techniques, a realistic example drawn from
the domain of forensic statistics is developed. In this domain the knowledge
engineering problems cited above are especially pronounced and expert estimates are commonplace. Moreover, robust conclusions are of unusually critical importance. An analysis of the resulting linguistic Bayesian network for
assessing evidential support in glass-transfer scenarios highlights the potential
utility of the approach
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Survival analysis in fishery management
Economic methodology is revisited to address issues in
fishery management, and survival analysis is suggested as an
analytical tool to solve the uncertainty problem in
evolutionary economics. Survival analysis can clarify in
statistical terms the impacts of fishery regulation and
economic and biological changes on the fisherman's final
decisions of entry and exit from the fishery. Two types Of
framework for survival analysis in fishery management are
presented to show how this can be applied to fishery
management. One type is the framework of the typical
regression analysis for market competition and the other type
is an extension to the dynamic fishery model. The evaluation
of survival analysis in fishery management is provideded. An
empirical application of the survival analysis, through the
Icelandic trawl fishery data, is given to show how to apply
the survival technique to fishery regulation
A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts
This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email.
The text introduces the conceptual (internalism, externalism), quantitative (probabilism) and logical perspectives (logics for reasoning about probabilities by Fagin, Halpern, Megiddo and MEL by Banerjee, Dubois) for the framework
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