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This paper discusses belief revision under uncertain inputs in the framework of possibility theory. Revision can be based on two possible definitions of the conditioning operation, one based on min operator which requires a purely ordinal…

Artificial Intelligence · Computer Science 2013-02-18 Didier Dubois , Henri Prade

It is commonly agreed that the success of future proof assistants will rely on their ability to incorporate computations within deduction in order to mimic the mathematician when replacing the proof of a proposition P by the proof of an…

Logic in Computer Science · Computer Science 2007-07-10 Frédéric Blanqui , Jean-Pierre Jouannaud , Pierre-Yves Strub

The fundamental updating process in the transferable belief model is related to the concept of specialization and can be described by a specialization matrix. The degree of belief in the truth of a proposition is a degree of justified…

Artificial Intelligence · Computer Science 2013-03-25 Frank Klawonn , Philippe Smets

The categorial approach to evidential reasoning can be seen as a combination of the probability kinematics approach of Richard Jeffrey (1965) and the maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a consequence of…

Artificial Intelligence · Computer Science 2013-03-26 Robert Kennes

The likelihood function plays a pivotal role in statistical inference; it is adaptable to a wide range of models and the resultant estimators are known to have good properties. However, these results hinge on correct specification of the…

Statistics Theory · Mathematics 2017-12-15 Adam Jaeger , Nicole Lazar

We develop and analyze $M$-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a non-asymptotic variational characterization of $f$-divergences, which allows the…

Statistics Theory · Mathematics 2016-11-18 XuanLong Nguyen , Martin J. Wainwright , Michael I. Jordan

This paper will focus on the process of 'fusing' several observations or models of uncertainty into a single resultant model. Many existing approaches to fusion use subjective quantities such as 'strengths of belief' and process these…

Artificial Intelligence · Computer Science 2020-07-28 Shawn C. Eastwood , Svetlana N. Yanushkevich

Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass…

Machine Learning · Computer Science 2020-04-20 Thierry Denoeux

We present and examine a result related to uncertainty reasoning, namely that a certain plausibility space of Cox's type can be uniquely embedded in a minimal ordered field. This, although a purely mathematical result, can be claimed to…

Artificial Intelligence · Computer Science 2015-11-24 Stefan Arnborg , Gunnar Sjödin

There is a growing need for the ability to analyse interval-valued data. However, existing descriptive frameworks to achieve this ignore the process by which interval-valued data are typically constructed; namely by the aggregation of…

Methodology · Statistics 2019-03-08 Xin Zhang , Boris Beranger , Scott A. Sisson

The standard method for the propagation of errors, based on a Taylor series expansion, is approximate and frequently inadequate for realistic problems. A simple and generic technique is described in which the likelihood is constructed…

High Energy Physics - Experiment · Physics 2015-06-25 J. Swain , L. Taylor

The Dempster--Shafer (DS) theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evidence. DS theory has been widely used in computer science and engineering applications, but has yet to reach the…

Methodology · Statistics 2010-11-04 Ryan Martin , Jianchun Zhang , Chuanhai Liu

We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain propositional…

Artificial Intelligence · Computer Science 2013-04-08 Gregory M. Provan

Several authors have explained that the likelihood ratio measures the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for…

Artificial Intelligence · Computer Science 2013-02-01 Paul-Andre Monney

Within the framework of evidence theory, the confidence functions of different information can be combined into a combined confidence function to solve uncertain problems. The Dempster combination rule is a classic method of fusing…

Statistical Finance · Quantitative Finance 2021-08-09 Tianxiang Zhan , Fuyuan Xiao

Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision…

Artificial Intelligence · Computer Science 2012-07-19 Leila Amgoud , Henri Prade

The set of subsystems of a finite quantum system (with variables in Z(n)) together with logical connectives, is a distributive lattice. With regard to this lattice, the (where P(m) is the projector to) obeys a supermodularity inequality,…

Quantum Physics · Physics 2015-06-23 A. Vourdas

We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the Dempster-Shafer theory of belief functions and possibility…

Artificial Intelligence · Computer Science 2022-02-17 Thierry Denoeux

We first discuss certain problems with the classical probabilistic approach for assessing forensic evidence, in particular its inability to distinguish between lack of belief and disbelief, and its inability to model complete ignorance…

Probability · Mathematics 2017-02-02 Timber Kerkvliet , Ronald Meester

This paper presents some ideas to reduce the computational cost of evidence-based robust design optimization. Evidence Theory crystallizes both the aleatory and epistemic uncertainties in the design parameters, providing two quantitative…

Computational Engineering, Finance, and Science · Computer Science 2012-07-17 Massimiliano Vasile , Edmondo Minisci , Quirien Wijnands