Bayesian Networks
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. The examples start from the simplest notions and gradually increase in complexity. Simple yet meaningful examples illustrate each step of the modelling process and discus… Celý popis ↓
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Bayesian Networks
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. The examples start from the simplest notions and gradually increase in complexity. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code.
These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. The following two chapters delve into dynamic networks (to model temporal data) and into networks
Parametre
- Výrobca
- Taylor & Francis Ltd
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