Stochastic relational processes: efficient inference and applications

DOI10.1007/s10994-010-5213-8zbMath1237.68169OpenAlexW2033468807MaRDI QIDQ413831

Niels Landwehr, Ingo Thon, Luc De Raedt

Publication date: 8 May 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-010-5213-8

zbMATH Keywords

time seriesMarkov processesstatistical relational learningCP-logicstochastic relational process

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27)

Related Items

Probabilistic logic programming for hybrid relational domains ⋮ \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs ⋮ Exploiting symmetries for scaling loopy belief propagation and relational training ⋮ Probabilistic (logic) programming concepts ⋮ Online event recognition over noisy data streams ⋮ Lifted Bayesian Filtering in Multiset Rewriting Systems ⋮ Unnamed Item ⋮ Lifted Dynamic Junction Tree Algorithm

Uses Software

• ProbLog
• PRISM

Cites Work

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• The independent choice logic for modelling multiple agents under uncertainty
• Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simpler ones
• Probabilistic inductive logic programming. Theory and applications
• Markov logic networks
• Representing Causal Information About a Probabilistic Process
• Graph-Based Algorithms for Boolean Function Manipulation
• Relational Sequence Learning
• Inductive Logic Programming