Learning Probabilistic Graphical Models in R 原版pdf by Bellot

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文件名称：Learning Probabilistic Graphical Models in R 原版pdf by Bellot
文件大小：3.29MB
文件格式：PDF
更新时间：2021-05-25 04:17:47
PGM AI Probabilistic graphical models is one of the most advanced techniques in machine learning to represent data and models in the real world with probabilities. In many instances, it uses the Bayesian paradigm to describe algorithms that can draw conclusions from noisy and uncertain real-world data. The book covers topics such as inference (automated reasoning and learning), which is automatically building models from raw data. It explains how all the algorithms work step by step and presents readily usable solutions in R with many examples. After covering the basic principles of probabilities and the Bayes formula, it presents Probabilistic Graphical Models(PGMs) and several types of inference and learning algorithms. The reader will go from the design to the automatic fitting of the model.