174,559 research outputs found
Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory
No full textCombining testimonial reports from independent and partially reliable information sources is an important problem of uncertain reasoning. Within the framework of Dempster-Shafer theory, we propose a general model of partially reliable sources which includes several previously known results as special cases. The paper reproduces these results, gives a number of new insights, and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information.Articl
John Shafer, on the Middle Loup River, below Woods Park, Custer County, Nebraska.
No full textIdentified, l to r: J.T. Shafer, John Shafer, Mrs. Jimie Shafer, H.G. Shannon, Mrs. H.G. Shannon, Mrs. Elizabeth McCullough holding a baby, Miss Rebecca Shafer, and mother, Rebecca Shafer and others
Induced aggregation operators in decision making with the Dempster-Shafer belief structure
No full textWe study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.aggregation operators, dempster-shafer belief structure, uncertainty, iowa operator, decision making
Pioneer personal history, Frank Marion Shafer
No full textTypescript of a biographical sketch of Frank Marion Shafer of Moab, Utah, from an interview. A native of Indiana, he headed west in 1885, and in 1888 settled at Moab. Typed by Winford Bunce of Moab, April 23, 193
The Dempster-Shafer Theory
No full textThe initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered through distributions of random sets. More furtive comparison has been with the traditional Bayesian theory, where D-S theory has been considered a generalisation of it (Schubert, 1994). Cobb and Shenoy (2003) direct its attention to the comparison of D-S theory and the Bayesian formulisation. Their conclusions are that they have the same expressive power, but that one technique cannot simply take the role of the other. The association with artificial intelligence (AI) is clearly outlined in Smets (1990), who at the time, acknowledged the AI community has started to show interest for what they call the Dempster-Shafer model. It is of interest that even then, they highlight that there is confusion on what type of version of D-S theory is considered. D-S theory was employed in an event driven integration reasoning scheme in Xia et al. (1997), associated with automated route planning, which they view as a very important branch in applications of AI. Liu (1999) investigated Gaussian belief functions and specifically considered their proposed computation scheme and its potential usage in AI and statistics. Huang and Lees (2005) apply a D-S theory model in natural-resource classification, comparing with it with two other AI models. Wadsworth and Hall (2007) considered D-S theory in a combination with other techniques to investigate site-specific critical loads for conservation agencies. Pertinently, they outline its positioning with respect to AI (p. 400); The approach was developed in the AI (artificial intelligence) community in an attempt to develop systems that could reason in a more human manner and particularly the ability of human experts to “diagnose” situations with limited information. This statement is pertinent here, since emphasis within the examples later given is more towards the general human decision making problem and the handling of ignorance in AI. Dempster and Kong (1988) investigated how D-S theory fits in with being an artificial analogy for human reasoning under uncertainty. An example problem is considered, the murder of Mr. White, where witness evidence is used to classify the belief in the identification of an assassin from considered suspects. The numerical analyses presented exposit a role played by D-S theory, including the different ways it can act on incomplete knowledge
Using Dempster-Shafer theory in data mining
No full textThe origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in their book “A Mathematical Theory of Evidence” developed Dempster’s work, including a more thorough explanation of belief functions, a more general term for DST. In summary, it is a methodology for evidential reasoning, manipulating uncertainty and capable of representing partial knowledge (Haenni & Lehmann, 2002; Kulasekere, Premaratne, Dewasurendra, Shyu, & Bauer, 2004; Scotney & McClean, 2003). The perception of DST as a generalisation of Bayesian theory (Shafer & Pearl, 1990), identifies its subjective view, simply, the probability of an event indicates the degree to which someone believes it. This is in contrast to the alternative frequentist view, understood through the “Principle of I sufficient reasoning”, whereby in a situation of ignorance a Bayesian approach is forced to evenly allocate subjective (additive) probabilities over the frame of discernment. See Cobb and Shenoy (2003) for a contemporary comparison between Bayesian and belief function reasoning. The development of DST includes analogies to rough set theory (Wu, Leung, & Zhang, 2002) and its operation within neural and fuzzy environments (Binaghi, Gallo, & Madella, 2000; Yang, Chen, & Wu, 2003). Techniques based around belief decision trees (Elouedi, Mellouli, & Smets, 2001), multi-criteria decision making (Beynon, 2002) and non-paramnteric regression (Petit-Renaud & Denoeux, 2004), utilise DST to allow analysis in the presence of uncertainty and imprecision. This is demonstrated, in this article, with the ‘Classification and Ranking belief Simplex’ (CaRBS) technique for object classification, see Beynon (2005a). </jats:p
H. G. Shannon, near Woods Parks, Custer County, Nebraska.
No full textIn 1938, Mr. John F. Shafer of Lincoln identified each of the individuals as (l. to r.): John Shafer, Lonna J. Shafer, Ele Hawkins (seated), Harvey G. Shannon, Thomas R. Shannon, John F. Shafer (seated in the foreground), Mrs. Harvey S. Shannon, and Eli Shannon (with the violin)
Harvey G. Shannon, near Woods Parks, Custer County, Nebraska
No full textThe photograph originally was identified as Thomas Shannon, but the identification was clarified by G.F. Shafer of Lincoln in 1946, who was Harvey Shannon's grandson. Pictured are: Thomas Shafer (foreground tipping his hat), Eli Shannon (young man holding the horses), Eli Hawkins (posed with the mules), John F. Shafer (boy on horseback), John S. Shafer (seated in the wagon), Mrs. Harvey Shannon (seated to the left in the carriage), and Louisa J. Shafer (seated to the right in the carriage)
Dual-Band Wearable Metallic Button Antennas and Transmission in Body Area Networks
No full textA dual-band metallic antenna with the appearance of a button on a pair of jeans for use with wearable computer networks, emergency rescue scenarios and future wireless medical applications is presented. The design operates at 2.4 GHz WLAN and the HiperLAN/2 bands and a parametric study is given to aid the design process together with measurement and simulation of the structure on a body. A study of transmission between pairs of on-body antennas is presented to give insight into on-body propagating line of sight and non-line of sight channels. A term 'body gain' is defined to quantify how the body attenuates the channel
Albert J. DiUlio, S.J., and Carol Shafer Failla, 1992
No full textMarquette University president Albert J. DiUlio, S.J. presents the Service Award to Carol Shafer Failla, 1992
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