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Bayesian networks in neuroscience: A survey
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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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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Cycles in Bayesian Networks [PDF]
International Journal of Electronics and Telecommunications, 2021 The article is devoted to some critical problems of using Bayesian networks for solving practical problems, in which graph models contain directed cycles.
Assem Shayakhmetova +4 more
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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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PLoS Computational Biology, 2021 In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data.
Ann-Kristin Becker +9 more
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Bayesian Quantum Neural Networks
IEEE Access, 2022 The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine ...
Nam Nguyen, Kwang-Cheng Chen
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Brain-Inspired Hardware Solutions for Inference in Bayesian Networks
Frontiers in Neuroscience, 2021 The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating ...
Leila Bagheriye, Johan Kwisthout
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Reverse engineering of genetic networks with Bayesian networks [PDF]
, 2003 This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described ...
Husmeier, D.
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