Web: http://arxiv.org/abs/2206.05490

June 17, 2022, 1:12 a.m. | Kiattikun Chobtham, Anthony C. Constantinou

cs.LG updates on arXiv.org arxiv.org

Discovering and parameterising latent confounders represent important and
challenging problems in causal structure learning and density estimation
respectively. In this paper, we focus on both discovering and learning the
distribution of latent confounders. This task requires solutions that come from
different areas of statistics and machine learning. We combine elements of
variational Bayesian methods, expectation-maximisation, hill-climbing search,
and structure learning under the assumption of causal insufficiency. We propose
two learning strategies; one that maximises model selection accuracy, and
another that …

arxiv bayesian discovery evidence lg networks

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY