English | 2025 | PDF | 6 MB | 297 Pages
Stephen C. Loftus, B0D25SG52V, 9780323954594, 978-0323954594, 978-0-323-95459-4, 0323954596, 044329111X, 9780443291111, 978-0443291111
An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences. Appropriate for students with some background in calculus and introductory statistics, particularly for nonstatisticians with a sufficient mathematical background, the text provides a gentle introduction to Bayesian ideas with a wide array of supporting examples from a variety of fields.
- Utilizes real datasets to illustrate Bayesian models and their results
- Guides readers on coding Bayesian models using the statistical software R, including a helpful introduction and supporting online resource
- Appropriate for an undergraduate statistics course, as well as for non-statisticians with sufficient mathematical background (integral and differential Calculus and an introductory Statistics course)
- Covers any more advanced topics which readers may not be familiar with, such as the basic idea of vectors and matrices
