A Generalization of Bayesian Inference

By Arthur P. Dempster. 1968

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Meimei Xia
2. Zeshui Xu
3. Dongling Xu
4. Huan Fu
5. Jianbo Yang
6. Changqing Zhang
7. Richard Jensen
8. Qiang Shen

Citations in the corpus

• Evidential Deep Learning to Quantify Classification Uncertainty (2018, methodology): “In this paper, we put our full focus on the uncertainty estimation problem and approach it from a Theory of Evidence perspective [7, 14].”
• Degrees of Belief (2009, methodology): “To represent a ternary probabilistic space, Dempster used a similar picture almost thirty years earlier in his most influential papers [12, 13].”
• The Boxer, the Wrestler, and the Coin Flip (2006, methodology): “The method of belief functions ( Dempster, 1967, 1968 ) has been proposed as a generalization of Bayesian inference that more directly allows the modeling of ignorance (Shafer, 1976).”
• Current map-matching algorithms for transport applications: State-of-the art and future research directions (2007, background): “They attempt to achieve this 2 See Dempster (1968) and Shafer (1976) for details accuracy of positioning by the efficient use of digital road maps.”
• A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living (2012, background): “Dempster–Shafer’s theory of evidence [27, 103] is considered to be able to represent ignorance due to lack of information, and to aggregate belief when new evidence is obtained.”
• IoT-Based Sensor Data Fusion for Occupancy Sensing Using Dempster–Shafer Evidence Theory for Smart Buildings (2017, background): “This paper adopts Dempster–Shafer evidence theory (DSET) as a mathematical model to fuse information coming from multiple heterogeneous data sources [2], [3].”
• Analyzing the combination of conflicting belief functions (2007, methodology): “Nevertheless many comments could be applied to the Dempsterian models, i.e., the model studied by Dempster [17] [18], those considered in Shafer's papers, the hint model [50], the probabilistic assumption model (PAS) [34].”
• The Dempster-Shafer calculus for statisticians (2008, background): “Another special case of DS combination is Bayes’s formula for combining prior and likelihood in statistical inference [4], where again the independence assumption is implicit and rarely examined.”