An Introductory Handbook of Bayesian Thinking - Brossura

Loftus, Stephen C.

Brossura

ISBN 10: 0323954596 ISBN 13: 9780323954594

Casa editrice: Academic Pr, 2024

Riassunto

As Bayesian techniques become more common across a variety of fields, it becomes important for experts in those fields to understand those methods. 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.

Su questo libro

Informazioni sull?autore

Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.

Dalla quarta di copertina

As Bayesian techniques become more common across a variety of fields, it becomes important for experts in those fields to understand those methods. 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.
The first half of the text introduces the basic rules of probability and random variables, decision making based on likelihoods, and concludes with transitioning from the frequentist to Bayesian paradigm of thinking. The second half of the text focuses on Bayesian models for specific situations, including hierarchical models for the mean and precision, regression, binomial/ordinal regression, and more. Throughout, real datasets are used to illustrate the models and their results. Additionally, readers are taught how to code up their models using the statistical software R, a basic introduction to which is provided.

Key Features

• 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
• Appropriate for an undergraduate introductory course, as well as a self-teaching manual for nonstatisticians with sufficient mathematical background (integral and differential calculus and an introductory statistics course)
• Covers foundational material, which readers may not be familiar with---such as probability, random variables, and probability distributions---as much as needed to foster understanding of core concepts

About the Author
Dr. Stephen C. Loftus

Analyst, Research & Development, Atlanta Braves Baseball Club

Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.

Dettagli dell'edizione

• Editore: Academic Pr
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 0323954596
• ISBN 13: 9780323954594
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 350
• Contatto del produttore: Academic Press; https://shop.elsevier.com/; ; 230 PARK AVENUE, SUITE 800; NY; NY; 10169; U.S.A.
• Persona responsabile: non disponibile