2010
ACML
ACML 2010
A Study of Approximate Inference in Probabilistic Relational Models
Abstract
We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set.
🚀
Conference Pioneer
— ACML 2010
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
📈
Trend Setter
— Probabilistic Modeling
🧭
Keyword Pioneer
— lazy aggregation
🐝
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, Speech & Audio
🐣
Hot Topic Early Bird
— bayesian network