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- W2296101049 abstract "In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context the amount of agent communication. To achieve this, we introduce Pseudo-Independence, a relaxed independence relation that quantitatively differentiates the various degrees of independence among nodes in a BN. We combine Pseudo-Independence with Context-Specific Independence to obtain a measure, Context-Specific Pseudo-Independence (CSPI), that determines the amount of required data that needs to be used to infer within the error bound. We then use a Conditional Probability Table-based generation search process that utilize CSPI to determine the minimal observation set. We present empirical results to demonstrate that bounded approximate inference can be made with fewer observations." @default.
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- W2296101049 date "2008-01-01" @default.
- W2296101049 modified "2023-10-16" @default.
- W2296101049 title "Finding Minimum Data Requirements Using Pseudo-independence." @default.
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