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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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期刊全稱Bayesian Networks in R
期刊簡稱with Applications in
影響因子2023Radhakrishnan Nagarajan,Marco Scutari,Sophie Lèbre
視頻videohttp://file.papertrans.cn/182/181869/181869.mp4
發(fā)行地址Represents 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
學(xué)科分類Use R!
圖書封面Titlebook: Bayesian Networks in R; with Applications in Radhakrishnan Nagarajan,Marco Scutari,Sophie Lèbre Book 2013 Springer Science+Business Media N
影響因子.Bayesian Networks in R with Applications in Systems Biology. is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using theapproaches presented in the book..
Pindex Book 2013
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Bayesian Networks in the Presence of Temporal Information,he fundamental ideas behind static Bayesian networks to model associations arising from the temporal dynamics between the entities of interest. This has to be contrasted with static Bayesian networks, which model the network structure from multiple independent realizations of the entities of a snaps
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Bayesian Network Inference Algorithms,yesian 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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Gergely Posfai,Gabor Magyar,Laszlo T. Koczy multivariate linear time series using dynamic Bayesian networks. Applications include modeling gene networks from expression data. Two broad classes of multivariate time series are considered: those whose statistical properties are invariant as a function of time and those whose properties do show
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