5 research outputs found
A betting interpretation for probabilities and Dempster-Shafer degrees of belief
There are at least two ways to interpret numerical degrees of belief in terms
of betting: (1) you can offer to bet at the odds defined by the degrees of
belief, or (2) you can judge that a strategy for taking advantage of such
betting offers will not multiply the capital it risks by a large factor. Both
interpretations can be applied to ordinary additive probabilities and used to
justify updating by conditioning. Only the second can be applied to
Dempster-Shafer degrees of belief and used to justify Dempster's rule of
combination.Comment: 20 page
Credal Networks under Epistemic Irrelevance
A credal network under epistemic irrelevance is a generalised type of
Bayesian network that relaxes its two main building blocks. On the one hand,
the local probabilities are allowed to be partially specified. On the other
hand, the assessments of independence do not have to hold exactly.
Conceptually, these two features turn credal networks under epistemic
irrelevance into a powerful alternative to Bayesian networks, offering a more
flexible approach to graph-based multivariate uncertainty modelling. However,
in practice, they have long been perceived as very hard to work with, both
theoretically and computationally.
The aim of this paper is to demonstrate that this perception is no longer
justified. We provide a general introduction to credal networks under epistemic
irrelevance, give an overview of the state of the art, and present several new
theoretical results. Most importantly, we explain how these results can be
combined to allow for the design of recursive inference methods. We provide
numerous concrete examples of how this can be achieved, and use these to
demonstrate that computing with credal networks under epistemic irrelevance is
most definitely feasible, and in some cases even highly efficient. We also
discuss several philosophical aspects, including the lack of symmetry, how to
deal with probability zero, the interpretation of lower expectations, the
axiomatic status of graphoid properties, and the difference between updating
and conditioning
A New Understanding of Subjective Probability and Its Generalization to Lower and Upper Prevision
This article introduces a new wa of understanding subjective probabilit and its generalization to lower and upper prevision