Tuning structure learning algorithms with out-of-sample and resampling strategies
K Chobtham, AC Constantinou - Knowledge and Information Systems, 2024 - Springer
One of the challenges practitioners face when applying structure learning algorithms to their
data involves determining a set of hyperparameters; otherwise, a set of hyperparameter …
data involves determining a set of hyperparameters; otherwise, a set of hyperparameter …
[PDF][PDF] The Bayesys data and Bayesian network repository
THE BAYESYS DATA AND BAYESIAN NETWORK REPOSITORY IS DISTRIBUTED AND
LICENSED FREE OF CHARGE IN THE HOPE IT WILL BE USEFUL. BECAUSE OF THIS …
LICENSED FREE OF CHARGE IN THE HOPE IT WILL BE USEFUL. BECAUSE OF THIS …
[HTML][HTML] Using GPT-4 to guide causal machine learning
Since its introduction to the public, ChatGPT has had an unprecedented impact. While some
experts praised AI advancements and highlighted their potential risks, others have been …
experts praised AI advancements and highlighted their potential risks, others have been …
Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes
Diabetes, a pervasive and enduring health challenge, imposes significant global
implications on health, financial healthcare systems, and societal well-being. This study …
implications on health, financial healthcare systems, and societal well-being. This study …
How much do we really know about Structure Learning from iid Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery
G Velev, S Lessmann - arXiv preprint arXiv:2409.19377, 2024 - arxiv.org
Nonlinear causal discovery from observational data imposes strict identifiability assumptions
on the formulation of structural equations utilized in the data generating process. The …
on the formulation of structural equations utilized in the data generating process. The …
The impact of variable ordering on Bayesian Network Structure Learning
NK Kitson, AC Constantinou - Data Mining and Knowledge Discovery, 2024 - Springer
Abstract Causal Bayesian Networks (CBNs) provide an important tool for reasoning under
uncertainty with potential application to many complex causal systems. Structure learning …
uncertainty with potential application to many complex causal systems. Structure learning …
Investigating potential causes of Sepsis with Bayesian network structure learning
B Petrungaro, NK Kitson, AC Constantinou - arXiv preprint arXiv …, 2024 - arxiv.org
Sepsis is a life-threatening and serious global health issue. This study combines knowledge
with available hospital data to investigate the potential causes of Sepsis that can be affected …
with available hospital data to investigate the potential causes of Sepsis that can be affected …
[PDF][PDF] Eliminating Variable Order Instability in Greedy Score-Based Structure Learning.
NK Kitson, AC Constantinou - International …, 2024 - raw.githubusercontent.com
Abstract Many Bayesian Network structure learning algorithms are unstable in that the learnt
graph is sensitive to arbitrary artefacts of the dataset, such as the ordering of columns (ie …
graph is sensitive to arbitrary artefacts of the dataset, such as the ordering of columns (ie …
Explicit and implicit knowledge-enhanced model for event causality identification
S Chen, K Mao - Expert Systems with Applications, 2024 - Elsevier
Abstract Event Causality Identification (ECI) aims at detecting the causal relation between 2
events, which is a challenging task due to the complexity of causal expressions and the …
events, which is a challenging task due to the complexity of causal expressions and the …
[PDF][PDF] The Bayesys user manual
A Constantinou - Queen Mary University of London, London, UK …, 2019 - constantinou.info
The Bayesys user manual Page 1 1 The Bayesys user manual Anthony C. Constantinoua, b
Version 3.61 (last revision: Jun 2024) a) Bayesian AI research lab, Machine Intelligence …
Version 3.61 (last revision: Jun 2024) a) Bayesian AI research lab, Machine Intelligence …