Learning neural causal models from unknown interventions
Promising results have driven a recent surge of interest in continuous optimization methods
for Bayesian network structure learning from observational data. However, there are …
for Bayesian network structure learning from observational data. However, there are …
Network structure learning under uncertain interventions
F Castelletti, S Peluso - Journal of the American Statistical …, 2023 - Taylor & Francis
Abstract Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the
network of dependencies among variables, a task which is of primary interest in many fields …
network of dependencies among variables, a task which is of primary interest in many fields …
Neural causal structure discovery from interventions
Recent promising results have generated a surge of interest in continuous optimization
methods for causal discovery from observational data. However, there are theoretical …
methods for causal discovery from observational data. However, there are theoretical …
Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction
YY Xu, F Yang, HB Shen - Bioinformatics, 2016 - academic.oup.com
Motivation: Bioimages of subcellular protein distribution as a new data source have attracted
much attention in the field of automated prediction of proteins subcellular localization …
much attention in the field of automated prediction of proteins subcellular localization …
Improved CCM for variable causality detection in complex systems
Abstract Convergent cross-mapping (CCM), has been largely implemented for variable
causality detection in complex systems like chemical process. However, this method is …
causality detection in complex systems like chemical process. However, this method is …
[PDF][PDF] Observing and intervening: Rational and heuristic models of causal decision making
B Meder, T Gerstenberg, Y Hagmayer… - The Open Psychology …, 2010 - pure.mpg.de
Recently, a number of rational theories have been put forward which provide a coherent
formal framework for modeling different types of causal inferences, such as prediction …
formal framework for modeling different types of causal inferences, such as prediction …
Repeated causal decision making.
Y Hagmayer, B Meder - Journal of Experimental Psychology …, 2013 - psycnet.apa.org
Many of our decisions refer to actions that have a causal impact on the external
environment. Such actions may not only allow for the mere learning of expected values or …
environment. Such actions may not only allow for the mere learning of expected values or …
Towards accurate root-alarm identification: The causal Bayesian network approach
Abnormalities in modern process industries are reported by alarms. Strong inter-
connectivities within different units of a plant lead to annunciations of multiple alarms in a …
connectivities within different units of a plant lead to annunciations of multiple alarms in a …
Data-driven methods for the detection of causal structures in process technology
In modern industrial plants, process units are strongly cross-linked with each other, and
disturbances occurring in one unit potentially become plant-wide. This can lead to a flood of …
disturbances occurring in one unit potentially become plant-wide. This can lead to a flood of …
[图书][B] Data-driven methods for fault localization in process technology
C Kühnert - 2013 - books.google.com
Control systems at production plants consist of a large number of process variables. When
detecting abnormal behavior, these variables generate an alarm. Due to the interconnection …
detecting abnormal behavior, these variables generate an alarm. Due to the interconnection …