Using Dempster-Shafer Belief Functions to Represent Degrees of Belief

1991 ◽  
Author(s):  
James M. Conley ◽  
Shawn P. Curley
Keyword(s):  
Belief Functions ◽  
Degrees Of Belief

Using Belief Functions to Represent Degrees of Belief

1994 ◽  
Vol 58 (2) ◽  
pp. 271-303 ◽  
Author(s):  
Shawn P. Curley ◽  
James I. Golden
Keyword(s):  
Belief Functions ◽  
Degrees Of Belief

The Metaphysics of Representation

2020 ◽  
Author(s):  
J. Robert G. Williams
Keyword(s):  
Representational Content ◽  
Physical World ◽  
Degrees Of Belief ◽  
Public Language ◽  
Language And Thought ◽  
Final Layer ◽  
The World ◽  
Interactive Relationship ◽  
Compositional Interpretation ◽  
The One

What is representation? How do the more primitive aspects of our world come together to generate it? How do different kinds of representation relate to one another? This book identifies the metaphysical foundations for representational facts. The story told is in three parts. The most primitive layer of representation is the ‘aboutness’ of sensation/perception and intention/action, which are the two most basic modes in which an individual and the world interact. It is argued that we can understand how this kind of representation can exist in a fundamentally physical world so long as we have an independent, illuminating grip on functions and causation. The second layer of representation is the ‘aboutness’ of (degrees of) belief and desire, whose representational content goes far beyond the immediate perceptable and manipulable environment. It is argued that the correct belief/desire interpretation of an agent is the one which makes their action-guiding states, given their perceptual evidence, most rational. The final layer of representation is the ‘aboutness’ of words and sentences, human artefacts with representational content. It is argued that one can give an illuminating account of the conditions under which a compositional interpretation of a public language like English is correct by appeal to patterns emerging from the attitudes conventionally expressed by sentences. The three-layer metaphysics of representation resolves long-standing underdetermination puzzles, predicts and explains patterns in the way that concepts denote, and articulates a delicate interactive relationship between the foundations of language and thought.

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

Econometric Forecasting Using Linear Regression and Belief Functions

2014 ◽  
pp. 304-312 ◽  
Author(s):  
Orakanya Kanjanatarakul ◽  
Philai Lertpongpiroon ◽  
Sombat Singkharat ◽  
Songsak Sriboonchitta
Keyword(s):  
Linear Regression ◽  
Belief Functions

scholarly journals Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions

2013 ◽  
Vol 14 (4) ◽  
pp. 504-520 ◽  
Author(s):  
Anthony Fiche ◽  
Jean-Christophe Cexus ◽  
Arnaud Martin ◽  
Ali Khenchaf
Keyword(s):  
Pattern Recognition ◽  
Stable Distribution ◽  
Belief Functions

Conditional Belief and the Ramsey Test

2002 ◽  
Vol 51 ◽  
pp. 215-232
Author(s):  
Scott Sturgeon
Keyword(s):  
Everyday Life ◽  
Common Sense ◽  
The Other ◽  
Coarse Grained ◽  
Degrees Of Belief ◽  
Conditional Belief ◽  
Ramsey Test ◽  
Fine Grained

Consider the frameS believes that—.Fill it with a conditional, sayIf you eat an Apple, you'll drink a Coke.what makes the result true? More generally, what facts are marked by instances ofS believes (A→C)?In a sense the answer is obious: beliefs are so marked. Yet that bromide leads directly to competing schools of thought. And the reason is simple.Common-sense thinks of belief two ways. Sometimes it sees it as a three-part affair. When so viewed either you believe, disbelieve, or suspend judgment. This take on belief is coarse-grained. It says belief has three flavours: acceptance, rejection, neither. But it's not the only way common-sense thinks of belief. Sometimes it's more subtle: ‘How strong is your faith?’ can be apposite between believers. That signals an important fact. Ordinary practice also treats belief as a fine-grained affair. It speaks of levels of confidence. It admits degrees of belief. It contains a fine-grained take as well. There are two ways belief is seen in everyday life. One is coarse-grained. The other is fine-grained.

Sign in / Sign up

Export Citation Format

Share Document