Bayesian statistics for beginners a step by step approach
Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.
| Main Authors: | , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Published: |
United Kingdom Oxford University Press
2019
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| Subjects: | Teoría bayesiana de decisiones estadísticas, Probabilidades, Estadística matemática, |
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