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Introduction to Bayesian Inference, Methods and Computation

Introduction to Bayesian Inference, Methods and Computation

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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
\n


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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