a primer on learning in bayesian networks for computational biology一个入门学习贝叶斯网络计算生物学.pdfVIP

Education A Primer on Learning in Bayesian Networks for Computational Biology * Chris J. Needham , James R. Bradford, Andrew J. Bulpitt, David R. Westhead the choice of model. This primer focuses on the use of BNs, which offer a solution to these issues. The use of Bayesian probability theory provides mechanisms for describing uncertainty and for adapting the number of parameters to the size of the data. Using a graphical representation provides a simple way to visualise the structure of a model. Inspection of models can provide valuable insights into the properties of the data and allow new models to be produced. Introduction Bayesian Networks Bayesian networks (BNs) provide a neat and compact In a graphical model representation, variables are representation for expressing joint probability distributions represented by nodes that are connected together by edges (JPDs) and for inference. They are becoming increasingly representing relationships between variables. Figure 1 important in the biological sciences for the tasks of inferring provides an example of a BN describing a gene regulation cellular networks [1], model