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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Knowledge & Reasoning and Machine Learning
📈 Trend Setter — Causal Inference
🧭 Keyword Pioneer — probabilistic belief revision
🐝 Cross-Pollinator — Artificial Intelligence, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization
🐣 Hot Topic Early Bird — probabilistic modeling