Belief Function Robustness in Estimation
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We consider the case in which the available knowledge does not allow to specify a precise probabilistic model for the prior and/or likelihood in statistical estimation. We assume that this imprecision can be represented by belief functions. Thus, we exploit the mathematical structure of belief functions and their equivalent representation in terms of closed convex sets of probability measures to derive robust posterior inferences.
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- 2.Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)Google Scholar
- 7.Molchanov, I.: Theory of random sets. Springer (2005)Google Scholar
- 9.Schweppe, F.C.: Recursive state estimation: Unknown but bounded errors and system inputs. In: Sixth Symposium on Adaptive Processes, vol. 6, pp. 102–107 (1967)Google Scholar