This tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems. Basic terminology, formal concepts, representational and inference issues will be discussed, starting from basic notions about probability theory, in such a way that the novice and the less skilled in the field will be able to follow the details. Further reading and software packages and frameworks will also be discussed
An Introduction to Probabilistic Graphical Models
Luigi Portinale
2024-01-01
Abstract
This tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems. Basic terminology, formal concepts, representational and inference issues will be discussed, starting from basic notions about probability theory, in such a way that the novice and the less skilled in the field will be able to follow the details. Further reading and software packages and frameworks will also be discussedFile in questo prodotto:
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