May 1, 2024
Updated May 11, 2025
20 minute read
Bayesian inference is a statistical method rooted in the idea of updating beliefs in the face of new evidence. At its core, it provides a mathematical framework for reasoning about uncertainty. Instead of viewing probability as a long-run frequency of events, Bayesian inference treats probability as a degree of belief about a proposition or an unknown quantity. This approach allows us to combine prior knowledge or beliefs with observed data to arrive at updated, more informed conclusions.
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Find a path to becoming a Bayesian Inference. Learn more at:
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Reading list
We've selected 14 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
Bayesian Inference.
Provides a comprehensive introduction to Bayesian theory. It covers the basics of Bayesian theory, as well as more advanced topics such as decision theory and Bayesian networks.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers the basics of Bayesian statistics, as well as more advanced topics such as graphical models and reinforcement learning.
Introduces probabilistic graphical models (PGMs), which are a powerful tool for representing and reasoning about complex systems. PGMs are used in a wide range of applications, including computer vision, natural language processing, and machine learning.
Provides a comprehensive introduction to Bayesian statistics. It covers the basics of Bayesian statistics, as well as more advanced topics such as hierarchical models and MCMC.
Provides a comprehensive introduction to Bayesian analysis. It covers the basics of Bayesian analysis, as well as more advanced topics such as decision theory and Bayesian networks.
Provides a comprehensive introduction to Bayesian nonparametrics. It covers the basics of Bayesian nonparametrics, as well as more advanced topics such as Dirichlet processes and hierarchical models.
Provides a practical introduction to computational Bayesian statistics. It covers the basics of computational Bayesian statistics, as well as more advanced topics such as MCMC and variational inference.
Provides a fun and accessible introduction to Bayesian statistics. It uses real-world examples to illustrate the concepts of Bayesian statistics, and it shows how Bayesian methods can be used to solve a variety of problems.
Provides a unique perspective on machine learning by combining Bayesian and optimization techniques. It covers a wide range of topics, from supervised learning to unsupervised learning.
Provides a lighthearted and entertaining introduction to Bayesian statistics. It uses humor and cartoons to explain the concepts of Bayesian statistics, and it shows how Bayesian methods can be used to solve a variety of problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/znqu3h/bayesian
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