ISBN: 9781009598446 - bayesian social science statistics: volume 2: getting productive (elements in quantitative and computational methods for the social sciences) (13 resultados)

Hardback. Condición: New. This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.…

Condición: New.

Condición: New.

Hardcover. Condición: Brand New. 116 pages. 6.00x0.31x9.00 inches. In Stock.

Condición: New.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.…

Hardback. Condición: New. This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.…

Hardcover. Condición: new. Hardcover. This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout. This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoretical principles and real-world applications to provide motivation and intuition. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Hardcover. Condición: new. Hardcover. This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout. This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoretical principles and real-world applications to provide motivation and intuition. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Buch. Condición: Neu. Bayesian Social Science Statistics | Jeff Gill (u. a.) | Buch | Englisch | 2026 | Cambridge University Press | EAN 9781009598446 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Condición: new. Hardcover. This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout. This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoretical principles and real-world applications to provide motivation and intuition. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …