Causality, Propensity, and Bayesian Networks [PDF]
Synthese, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gillies, D, Gillies, DA
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CausNet-partial: 'Partial Generational Orderings' based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints. [PDF]
PLoS ONEIn our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. This 'generational orderings' based dynamic programming algorithm-CausNet-efficiently searches the space of possible ...
Nand Sharma, Joshua Millstein
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Bayesian networks in neuroscience: A survey [PDF]
Frontiers in Computational Neuroscience, 2014 Bayesian networks are a type of probabilistic graphical modelslie at the intersection between statistics and machine learning.They have been shown to be powerful tools to encode dependence relationshipsamong the variables of a domain under uncertainty ...
Concha eBielza, Pedro eLarrañaga
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Prognostic Modelling with Dynamic Bayesian Networks [PDF]
, 2009 In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not ...
McNaught, Ken R., Zagorecki, A.
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Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters [PDF]
, 2012 To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes.
Husmeier, D., Grzegorczyk, M.
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Practicalities of Bayesian network modeling for nuclear data evaluation with the nucdataBaynet package [PDF]
EPJ Web of Conferences, 2023 Bayesian networks are a helpful abstraction in the modelization of the relationships between different variables for the purpose of uncertainty quantification.
Schnabel Georg
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Using consensus bayesian network to model the reactive oxygen species regulatory pathway. [PDF]
PLoS ONE, 2013 Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data ...
Liangdong Hu, Limin Wang
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Learning oncogenetic networks by reducing to mixed integer linear programming. [PDF]
PLoS ONE, 2013 Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events.
Hossein Shahrabi Farahani +1 more
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Bayesian Networks in Radiology [PDF]
Radiology: Artificial Intelligence, 2023 A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values.
Shawn X. Ma +6 more
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Bayesian Approach to Linear Bayesian Networks
CoRR, 2023 This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix.
Seyong Hwang +3 more
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