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2008 NIPS NeurIPS 2008

Improving on Expectation Propagation

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐣 Hot Topic Early Bird — variational inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
📈 Trend Setter — Variational Inference

Authors

Manfred Opper , Ulrich Paquet , Ole Winther

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Bayesian Inference Artificial Intelligence > Bayesian & Probabilistic > Variational Inference

Keywords

approximate inference variational inference bayesian learning probabilistic inference message passing expectation propagation
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