Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events
that may cause catastrophic accidents and heavy financial losses. This study develops a …

In the past couple of years, various approaches to representing and quantifying different
types of predictive uncertainty in machine learning, notably in the setting of classification …

The representation and quantification of uncertainty has received increasing attention in
machine learning in the recent past. The formalism of credal sets provides an interesting …

Adequate uncertainty representation and quantification have become imperative in various
scientific disciplines, especially in machine learning and artificial intelligence. As an …

One of the hardest challenges in building a realistic Bayesian Network (BN) model is to
construct the node probability tables (NPTs). Even with a fixed predefined model structure …

Bayesian Network (BN) is often criticized for demanding a large number of
crisp/exact/precise conditional probability numbers which, due to the lack of statistics, have …

Uncertainty quantification and robustness to distribution shifts are important goals in
machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) …

Background In non-small-cell lung cancer radiotherapy, radiation pneumonitis≥ grade 2
(RP2) depends on patients' dosimetric, clinical, biological and genomic characteristics …

Safety and Reliability–Theory and Applications contains the contributions presented at the
27th European Safety and Reliability Conference (ESREL 2017, Portorož, Slovenia, June 18 …

Uncertainty quantification is a critical aspect of machine learning models, providing
important insights into the reliability of predictions and aiding the decision-making process in …