Abstract
Chapters 2 and 3 discussed the importance of learning the structure and the parameters of Bayesian networks from observational and interventional data sets. Bayesian 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 will introduce inferential techniques for static and dynamic Bayesian networks and their applications to gene expression profiles.
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Nagarajan, R., Scutari, M., Lèbre, S. (2013). Bayesian Network Inference Algorithms. In: Bayesian Networks in R. Use R!, vol 48. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6446-4_4
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DOI: https://doi.org/10.1007/978-1-4614-6446-4_4
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