Amazon cover image
Image from Amazon.com

Introduction to bayesian statistics / William M. Bolstad, James M. Curran

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Canada : Wiley, 2017Edition: 3rd edDescription: 601 tr. ; 23 cmISBN:
  • 9781118091562
Subject(s): Theme: Callnumber:
  • 519.5 B6389W
Abstract: In this brief introductory chapter, we sought to inform readers new to Bayesian statistics about the fundamental concepts in Bayesian analyses. The most important take-home messages to remember are that in Bayesian statistics, the analysis starts with an explicit formulation of prior beliefs that are updated with the observed data to obtain a posterior distribution. The posterior distribution is then used to make inferences about probable values of a given parameter (or set of parameters). Furthermore, Bayes Factors allow for comparison of non-nested models, and it is possible to compute the amount of support for the null hypothesis, which cannot be done in the frequentist framework. Subsequent chapters in this volume make use of Bayesian methods for obtaining posteriors of parameters of interest, as well as Bayes Factors.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Call number Copy number Status Date due Barcode
Sách ngoại văn Thư viện Uneti - Địa điểm Lĩnh Nam P. Đọc mở Lĩnh Nam 519.5 B6389W (Browse shelf(Opens below)) 1 Available 000031149
Sách ngoại văn Thư viện Uneti - Địa điểm Lĩnh Nam P. Đọc mở Lĩnh Nam 519.5 B6389W (Browse shelf(Opens below)) 2 Available 000033601

In this brief introductory chapter, we sought to inform readers new to Bayesian statistics about the fundamental concepts in Bayesian analyses. The most important take-home messages to remember are that in Bayesian statistics, the analysis starts with an explicit formulation of prior beliefs that are updated with the observed data to obtain a posterior distribution. The posterior distribution is then used to make inferences about probable values of a given parameter (or set of parameters). Furthermore, Bayes Factors allow for comparison of non-nested models, and it is possible to compute the amount of support for the null hypothesis, which cannot be done in the frequentist framework. Subsequent chapters in this volume make use of Bayesian methods for obtaining posteriors of parameters of interest, as well as Bayes Factors.

There are no comments on this title.

to post a comment.

QRcode