Efficient Symbolic Integration for Probabilistic Inference
| العنوان: | Efficient Symbolic Integration for Probabilistic Inference |
|---|---|
| المؤلفون: | Martin Mladenov, Samuel Kolb, Kristian Kersting, Scott Sanner, Vaishak Belle |
| المصدر: | IJCAI Kolb, S, Mladenov, M, Sanner, S, Belle, V & Kersting, K 2018, Efficient Symbolic Integration for Probabilistic Inference . in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence . Freiburg, Germany, pp. 5031-5037, 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13/07/18 . https://doi.org/10.24963/ijcai.2018/698 |
| بيانات النشر: | International Joint Conferences on Artificial Intelligence Organization, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | business.industry, Computer science, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, Artificial intelligence, Probabilistic inference, business, Symbolic integration |
| الوصف: | Weighted model integration (WMI) extends weighted model counting (WMC) to the integration of functions over mixed discrete-continuous probability spaces. It has shown tremendous promise for solving inference problems in graphical models and probabilistic programs. Yet, state-of-the-art tools for WMI are generally limited either by the range of amenable theories, or in terms of performance. To address both limitations, we propose the use of extended algebraic decision diagrams (XADDs) as a compilation language for WMI. Aside from tackling typical WMI problems, XADDs also enable partial WMI yielding parametrized solutions. To overcome the main roadblock of XADDs -- the computational cost of integration -- we formulate a novel and powerful exact symbolic dynamic programming (SDP) algorithm that seamlessly handles Boolean, integer-valued and real variables, and is able to effectively cache partial computations, unlike its predecessor. Our empirical results demonstrate that these contributions can lead to a significant computational reduction over existing probabilistic inference algorithms. |
| وصف الملف: | application/pdf |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e12ab8b9c87d9e2699ec15dfbbd84648 https://doi.org/10.24963/ijcai.2018/698 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....e12ab8b9c87d9e2699ec15dfbbd84648 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |