Nick Heard
Introduction to Bayesian Inference, Methods and Computation
Introduction to Bayesian Inference, Methods and Computation
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Excellent- More about Introduction to Bayesian Inference, Methods and Computation
The course covers Bayesian statistical methods,from fundamental philosophy to advanced topics,with Python and Stan code samples. It teaches model assessment and choice.
\n Format: Hardback
\n Length: 169 pages
\n Publication date: 18 October 2021
\n Publisher: Springer Nature Switzerland AG
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These comprehensive lecture notes offer a thorough and concise introduction to Bayesian statistical methods. The course delves into the core philosophy and principles of Bayesian inference, encompassing the rationale behind constructing prior/likelihood models, which are fundamental to Bayesian approaches. It progresses to cover advanced topics such as nonparametrics, Gaussian processes, and latent factor models, providing practical examples and code snippets written in Python and Stan. Moreover, the reader gains valuable skills in assessing model fit and selecting the most suitable modeling approaches. By the end of this course, participants will have a solid foundation in Bayesian statistics and be equipped to apply these techniques in various real-world applications.
Introduction
Prior/Likelihood Model Construction
Bayesian Inference Algorithms
Advanced Topics
Practical Examples
Assessing Model Fit
Choosing Between Rival Modeling Approaches
Conclusion
\n Weight: 420g\n
Dimension: 160 x 243 x 17 (mm)\n
ISBN-13: 9783030828073\n
Edition number: 1st ed. 2021\n
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