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Improving on Expectation Propagation

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

We develop as series of corrections to Expectation Propagation (EP), which is one of the most popular methods for approximate probabilistic inference. These corrections can lead to improvements of the inference approximation or serve as a sanity check, indicating when EP yields unrealiable results.

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Keywords

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
1087989309965012771
v2026.09.13