Open World Dempster-Shafer Theory/The Transferable Belief Model with Intervals: A Practitioner's Guide to DST and TBM
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Dempster-Shafer theory (DST) is a mathematical framework that allows for uncertainty or ignorance to be quantified and included when making predictions from evidence. This is in contrast to Bayesian theory, which does not allow for any quantification of ignorance. The framework is described in great detail in [7]. DST is particularly useful for problems where the inclusion of additional evidence (for example, data from another sensor) could lead to a different conclusion. Thus, it is a useful data fusion method, especially in applications not suited to maximum likelihood or maximum a posteriori estimations due to limited samples or incomplete prior knowledge.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2558017
- Report Number(s):
- LA-UR--25-23655
- Country of Publication:
- United States
- Language:
- English
Similar Records
Combination of Evidence in Dempster-Shafer Theory
A Layered Dempster-Shafer Approach to Scenario Construction and Analysis
Information integration using belief functions.
Technical Report
·
Sun Mar 31 23:00:00 EST 2002
·
OSTI ID:800792
A Layered Dempster-Shafer Approach to Scenario Construction and Analysis
Conference
·
Wed May 23 00:00:00 EDT 2007
·
OSTI ID:908737
Information integration using belief functions.
Conference
·
Mon Dec 31 23:00:00 EST 2001
·
OSTI ID:976104