Titlebook: Bayesian Networks in R; with Applications in Radhakrishnan Nagarajan,Marco Scutari,Sophie Lèbre Book 2013 Springer Science+Business Media N

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Myelin

Moderate

Zuzana Krivá,Angela Handlovi?ováyesian inference on the other hand is often a follow-up to Bayesian network learning and deals with inferring the state of a set of variables given the state of others as evidence. Such an approach eliminates the need for additional experiments and is therefore extremely helpful. In this chapter, we

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Piotr Kulczycki,Piotr A. Kowalskit is polynomial even for sparse networks. Even though newer algorithms are designed to improve scalability, it is unfeasible to analyze data containing more than a few hundreds of variables. Parallel computing provides a way to address this problem by making better use of modern hardware..In this ch

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Radhakrishnan Nagarajan,Marco Scutari,Sophie LèbreRepresents a unique combination of introduction to concepts and examples from open-source R software.Each chapter is accompanied by examples and exercises with solutions for enhanced understanding and

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https://doi.org/10.1007/978-1-4614-6446-4Bayes; Bayesian Theory; Graph Theory; Modeling; R; Systems Biology

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978-1-4614-6445-7Springer Science+Business Media New York 2013

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