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Second-order Bayesian networks extend Bayesian networks by incorporating uncertainty in the conditional probabilities. This paper develops a method for inference in a binary second-order Bayesian network with a singly-connected graph that builds upon the message-passing algorithm for regular belief propagation by leveraging recent developments in subjective logic. The method applies the moment-matching...
Subjective Bayesian networks extend Bayesian networks by incorporating uncertainty in the conditional probabilities. This paper develops subjective belief propagation (SBP) that extends regular belief propagation (BP) to efficiently infer uncertain marginal probabilities in subjective Bayesian networks. It is shown that SBP's runtime exhibits only slightly slower performance than standard BP but is...
Sensors and information sources can produce conflicting evidence for various reasons, including errors and deception. When conflicting evidence is received, some sources produce more accurate evidence than others with regard to the ground truth. The reliability of sources can be expressed by assigning a level of trust to each source. In this situation, multiple fusion strategies can be applied: one...
This work expands the concept of conditional reasoning in subjective logic between two variables to reasoning over multiple variables as a step to eventually realize efficient means for inference over subjective networks, i.e. Bayesian networks with uncertain marginal and conditional probabilities. Three new inference methods are introduced for a three node subjective network. Monte Carlo simulations...
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