EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Bayesian Social Science Statistics From The Very Beginning Jeff Gill

Name: Bayesian Social Science Statistics From The Very Beginning Jeff Gill
SKU: BELL-92648016
Price: 31.00 USD
Availability: InStock
Rating: 5 (18 reviews)

SKU: BELL-92648016

$ 31.00 ~~$ 45.00~~ (-31%)

5.0

18 reviews

Bayesian Social Science Statistics From The Very Beginning Jeff Gill instant download after payment.

Publisher: Cambridge University Press

File Extension: PDF

File size: 2.09 MB

Author: Jeff Gill, Le Bao

ISBN: 9781009494694, 1009494694

Language: English

Year: 2025

Product desciption

Bayesian Social Science Statistics From The Very Beginning Jeff Gill by Jeff Gill, Le Bao 9781009494694, 1009494694 instant download after payment.

In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than that in a basic statistics course. At the end of the process, readers will understand the core tenets of Bayesian theory and practice in a way that enables them to specify, implement, and understand models using practical social science data. Chapters will cover theoretical principles and real-world applications that provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in both R and Python is provided throughout.