An Introductory Handbook of Bayesian Thinking (Paperback)

Stephen C. Loftus

ISBN 10: 0323954596 / ISBN 13: 9780323954594

Verlag: Elsevier Science & Technology, Oxford, 2024

Beschreibung

Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Bestandsnummer des Verkäufers 9780323954594

Bibliografische Details

• Titel: An Introductory Handbook of Bayesian Thinking (Paperback)
• Verlag: Elsevier Science & Technology, Oxford
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 0323954596
• ISBN-13: 9780323954594
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.