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

尽忠

Myelin

Moderate

Zuzana Krivá,Angela Handlovičováyesian 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

Dri727

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

TAIN

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

粗鲁性质

Insufficient

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

Breach

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

Obituary

后天习得

9楼