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Directed Acyclic Graphs (DAGs)

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May 14, 2024 4 minute read
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Directed Acyclic Graphs (DAGs) are mathematical structures with an increasingly broad application in technology and the sciences. Ubiquitous in computer science, DAGs have applications in everything from Big Data to computer networking, and from operations research to computational biology. Understanding what DAGs are, how they are represented, and how to work with them is a valuable skill for professionals in these and many other fields.

Why Learn About Directed Acyclic Graphs (DAGs)?

There are many reasons why one might want to learn about Directed Acyclic Graphs (DAGs). Some of the most common reasons include:

Path to Directed Acyclic Graphs (DAGs)

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Reading list

We've selected five books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Directed Acyclic Graphs (DAGs).
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Causal Inference in Statistics
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Introduces the fundamental concepts and methods of causal inference, with a focus on the role of DAGs in understanding and modeling causal relationships.
Causal Inference in Statistics - A Primer
Paperback
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Causal Inference in Statistics: A Primer
Kindle Edition
$$
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Probabilistic Graphical Models
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Covers a wide range of topics related to graphical models, including DAGs, Bayesian networks, and Markov random fields, providing a theoretical foundation and practical algorithms for working with them.
Probabilistic Graphical Models: Principles and...
Hardcover
$$$$
Probabilistic Graphical Models: Principles and...
Kindle Edition
$$$
Learning in Graphical Models
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Covers a wide range of topics related to graphs and probabilistic models in artificial intelligence, including the use of DAGs in Bayesian networks and decision-making.
Graphical Models, Exponential Families, and...
Paperback
$$$$
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Bayesian Data Analysis, Third Edition
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Explores the use of Bayesian methods for causal inference, including the use of DAGs to represent causal relationships and perform probabilistic reasoning.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
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Reinforcement Learning, second edition
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Covers reinforcement learning, which utilizes DAGs to represent the state space and reward structure of an environment.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
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