Probabilistic Graphical Models: On Reasoning, Learning, and Revision (Extended Abstract)

Rudolf Kruse

2022

Abstract

Probabilistic Graphical Models are of high relevance for complex industrial applications. The Bayesian network and the Markov network approach are the most prominent representatives and an important tool to structure uncertain knowledge about high dimensional domains. This extended abstract serves to highlight that the decomposition of the underlying high dimensional spaces turns out to be useful to make reasoning, learning and revision in such domains feasible. The methods are explained by using a real-world industrial application from automotive industry.

Paper Citation

in Harvard Style

Kruse R. (2022). Probabilistic Graphical Models: On Reasoning, Learning, and Revision (Extended Abstract). In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: IC3K, ISBN 978-989-758-614-9, pages 9-10. DOI: 10.5220/0011598200003335

in Bibtex Style

@conference{ic3k22,
author={Rudolf Kruse},
title={Probabilistic Graphical Models: On Reasoning, Learning, and Revision (Extended Abstract)},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: IC3K,},
year={2022},
pages={9-10},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011598200003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: IC3K,
TI - Probabilistic Graphical Models: On Reasoning, Learning, and Revision (Extended Abstract)
SN - 978-989-758-614-9
AU - Kruse R.
PY - 2022
SP - 9
EP - 10
DO - 10.5220/0011598200003335