2010
NIPS
NeurIPS 2010
Probabilistic Belief Revision with Structural Constraints
Abstract
Experts (human or computer) are often required to assess the probability of uncertain events. When a collection of experts independently assess events that are structurally interrelated, the resulting assessment may violate fundamental laws of probability. Such an assessment is termed incoherent. In this work we investigate how the problem of incoherence may be affected by allowing experts to specify likelihood models and then update their assessments based on the realization of a globally-observable random sequence.
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Interdisciplinary Bridge
— Artificial Intelligence and Knowledge & Reasoning and Machine Learning
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Trend Setter
— Causal Inference
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Keyword Pioneer
— probabilistic belief revision
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Cross-Pollinator
— Artificial Intelligence, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization
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Hot Topic Early Bird
— probabilistic modeling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Bayesian Inference
Knowledge & Reasoning > Reasoning > Causal Inference
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Artificial Intelligence > Core AI > Knowledge Representation