2013/03/27 by Kathryn Blackmond Laskey, Laskey, Kathryn Blackmond
Arts and Humanities · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Decision-Making and Behavioral Economics #Epistemology, Ethics, and Metaphysics #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1304.2715
openalex publication_date 2013/03/27 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
In the canonical examples underlying Shafer-Dempster theory, beliefs over the\nhypotheses of interest are derived from a probability model for a set of\nauxiliary hypotheses. Beliefs are derived via a compatibility relation\nconnecting the auxiliary hypotheses to subsets of the primary hypotheses. A\nbelief function differs from a Bayesian probability model in that one does not\ncondition on those parts of the evidence for which no probabilities are\nspecified. The significance of this difference in conditioning assumptions is\nillustrated with two examples giving rise to identical belief functions but\ndifferent Bayesian probability distributions.\n