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概率论书籍推荐知乎
妍纯
2024-05-19
【生活】
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Recommended Books on Probability Programming
Probability programming is an interdisciplinary field that combines principles of probability theory and programming to solve complex problems involving uncertainty. Whether you are a beginner or an advanced practitioner, here are some highly recommended books that can help you delve deeper into this fascinating area:
This book provides an introduction to Bayesian methods and probabilistic programming using Python and the PyMC library. It's suitable for beginners and includes practical examples to help you understand the concepts.
Considered a classic in the field, this book covers Bayesian data analysis comprehensively. It's a great resource for understanding the theory behind Bayesian methods and applying them to realworld problems.
This book provides a thorough introduction to probabilistic graphical models, which are widely used in machine learning and artificial intelligence. It covers both the theoretical foundations and practical applications of graphical models.
While not solely focused on probabilistic programming, this book offers a comprehensive overview of machine learning from a probabilistic standpoint. It covers a wide range of topics, including Bayesian methods, graphical models, and probabilistic inference.
This book provides a handson introduction to Bayesian data analysis using R, JAGS, and Stan. It's suitable for beginners and includes plenty of examples and exercises to reinforce learning.
Another excellent book by DavidsonPilon, this one focuses on practical aspects of probabilistic programming and Bayesian inference. It's written in a tutorial style and is ideal for those looking to apply Bayesian methods to their projects.
This book provides a comprehensive introduction to probabilistic programming techniques for machine learning applications. It covers both the theory and implementation of probabilistic models using frameworks like TensorFlow Probability and Pyro.
David Barber's book offers a unified treatment of Bayesian methods and machine learning algorithms. It covers topics such as Bayesian networks, Gaussian processes, and variational inference, making it suitable for readers interested in both theory and practice.
These books cover a wide range of topics within probability programming, from basic concepts to advanced techniques. Depending on your level of expertise and specific interests, you can choose the ones that best suit your needs. Happy reading and exploring the fascinating world of probability programming!