A survey of Bayesian Network structure learning
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
[HTML][HTML] A review of causal inference for biomedical informatics
Causality is an important concept throughout the health sciences and is particularly vital for
informatics work such as finding adverse drug events or risk factors for disease using …
informatics work such as finding adverse drug events or risk factors for disease using …
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-
Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and …
Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and …
[PDF][PDF] Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation.
We present an algorithmic framework for learning local causal structure around target
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
Medical decision support using machine learning for early detection of late-onset neonatal sepsis
Objective The objective was to develop non-invasive predictive models for late-onset
neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR) …
neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR) …
Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD
To date, studies of biological risk factors have revealed inconsistent relationships with
subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the …
subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the …
Feature selection with the R package MXM: discovering statistically equivalent feature subsets
The statistically equivalent signature (SES) algorithm is a method for feature selection
inspired by the principles of constraint-based learning of Bayesian networks. Most of the …
inspired by the principles of constraint-based learning of Bayesian networks. Most of the …
[PDF][PDF] Active learning of causal networks with intervention experiments and optimal designs
YB He, Z Geng - Journal of Machine Learning Research, 2008 - jmlr.org
The causal discovery from data is important for various scientific investigations. Because we
cannot distinguish the different directed acyclic graphs (DAGs) in a Markov equivalence …
cannot distinguish the different directed acyclic graphs (DAGs) in a Markov equivalence …
Causal feature selection
I Guyon, C Aliferis - Computational methods of feature selection, 2007 - taylorfrancis.com
The present chapter makes an argument in favor of understanding and utilizing the notion of
causality for feature selection: from an algorithm design perspective, to enhance …
causality for feature selection: from an algorithm design perspective, to enhance …
A new hybrid method for learning bayesian networks: Separation and reunion
Most existing algorithms for learning Bayesian networks (BNs) can be categorized as
constraint-based or score-based methods. Constraint-based algorithms use conditional …
constraint-based or score-based methods. Constraint-based algorithms use conditional …