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Belief Calculus

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arxiv cs/0606029 v1 pith:RVMDMK57 submitted 2006-06-07 cs.AI

classification cs.AI
keywords beliefcalculusfunctionsbeliefsopinionsprobabilitybasicbeta
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In Dempster-Shafer belief theory, general beliefs are expressed as belief mass distribution functions over frames of discernment. In Subjective Logic beliefs are expressed as belief mass distribution functions over binary frames of discernment. Belief representations in Subjective Logic, which are called opinions, also contain a base rate parameter which express the a priori belief in the absence of evidence. Philosophically, beliefs are quantitative representations of evidence as perceived by humans or by other intelligent agents. The basic operators of classical probability calculus, such as addition and multiplication, can be applied to opinions, thereby making belief calculus practical. Through the equivalence between opinions and Beta probability density functions, this also provides a calculus for Beta probability density functions. This article explains the basic elements of belief calculus.

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Cited by 1 Pith paper

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  1. Vehicle Rebalancing Under Adherence Uncertainty

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    A vehicle rebalancing framework that models driver preferences and evolving trust via Thompson Sampling outperforms adherence-agnostic baselines in simulation.

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