May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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11. Bayesian Networks
11.1 Introduction
Graphical models are a rich subject, and several excellent books focus on them. Koller and Friedman’s Probabilistic Graphical Models covers the subject in-depth, and Murphy’s Machine Learning: A Probabilistic Perspective uses them to teach machine learning. Graphical models provide a powerful framework for many modern algorithms for matrix factorization and text analysis.
Graphical models are what they sound like: models for systems that are described by graphs, that is, nodes and edges. Each node is a variable, and each edge represents dependence between variables. These models can be directed or undirected. Along with the graph, there is generally a list of probability distributions on each of the ...
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