Research map / Probabilistic and scientific modeling / Variational and generative sampling
Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Active Learning of Continuous-time Bayesian Networks through Interventions (ICML 2021 · similarity 0.88)
- Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data (NeurIPS 2019 · similarity 0.88)
- Continuous Time Bayesian Networks with Clocks (ICML 2020 · similarity 0.87)
- Relational Learning with Variational Bayes (ICLR 2022 · similarity 0.86)
- The Complexity of Bayesian Network Learning: Revisiting the Superstructure (NeurIPS 2021 · similarity 0.86)
- Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data (NeurIPS 2018 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
