2020
PGM
PGM 2020
Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping
Abstract
Dry-bulk shipping is crucial for a functioning global trade economy. Thus, additional research is highly relevant to further improve bulk shipping operations. Dry-bulk shipping involves many entities interacting with each other in an uncertain environment that changes over time. To assist dry-bulk vessel operators in how to position their vessels, efficient query answering and decision support is necessary. Therefore, we investigate existing modelling formalism and inference algorithms regarding which aspects of dry-bulk shipping are already realisable. Although not all challenges are already well-understood, we show that a lifted dynamic approach tackles most of the challenges involved in handling dry-bulk shipping.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Data Science & Analytics > Applications > Risk Management
Machine Learning > Bayesian & Probabilistic > Bayesian Networks