An Introduction to Applied Bayesian Modeling Alicia A. Johnson's book is a study book that offers a clear introduction to applied Bayesian modeling. It is aimed at advanced undergraduate students and professionals with comparable prior knowledge, and covers practical applications of Bayesian modeling within statistics.
The book goes in depth into data-driven examples and emphasizes an iterative model-building process. The content supports learning from fundamental to complex, hierarchical models, including Markov chain Monte Carlo simulations. All relevant R code, including tools from RStan and the bayesrules package, is provided as an integral part.
Features
- Practical application of Bayesian modeling through data-driven examples and exercises
- Focus on the model-building and evaluation cycle
- Coverage of multivariable regression and classification models
- Introduction to Markov chain Monte Carlo simulations
- Integration of R code and statistical packages
- Accessible presentation of technical Bayesian concepts
- Support through theoretical grounding in Bayesian inference
Specifications
- Author: Alicia A. Johnson
- Publisher: Taylor & Francis Ltd
- Imprint: Chapman & Hall/CRC
- Publication date: 2022-03-04
- Number of pages: 544
- ISBN: 9780367255398
- Subject: Bayesian inference
- BISAC: MATHEMATICS / Probability & Statistics / Bayesian Analysis