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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2197-5736 and exercises with solutions for enhanced understanding and.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

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Zuzana Krivá,Angela Handlovičováe state of others as evidence. Such an approach eliminates the need for additional experiments and is therefore extremely helpful. In this chapter, we will introduce inferential techniques for static and dynamic Bayesian networks and their applications to gene expression profiles.

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Introduction,th other Use R!-series books, a brief introduction to the . environment and basic . programming is also provided. Some background in probability theory and programming is assumed. However, the necessary references are included under the respective sections for a more complete treatment.

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Bayesian Network Inference Algorithms,e state of others as evidence. Such an approach eliminates the need for additional experiments and is therefore extremely helpful. In this chapter, we will introduce inferential techniques for static and dynamic Bayesian networks and their applications to gene expression profiles.

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Bayesian Networks in the Absence of Temporal Information,to model the dependencies between the variables in static data. In this chapter, we will introduce the essential definitions and properties of static Bayesian networks. Subsequently, we will discuss existing Bayesian network learning algorithms and illustrate their applications with real-world examples and different . packages.

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Parallel Computing for Bayesian Networks,apter we will provide a brief overview of the history and the fundamental concepts of parallel computing, and we will examine their applications to Bayesian network learning and inference using the . package.

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