Return to search

Making Models with Bayes

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects the model. We will also make a comparison between the Bayesian approach and the traditional frequentist approach to data analyses.

Identiferoai:union.ndltd.org:csusb.edu/oai:scholarworks.lib.csusb.edu:etd-1661
Date01 December 2017
CreatorsOlid, Pilar
PublisherCSUSB ScholarWorks
Source SetsCalifornia State University San Bernardino
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses, Projects, and Dissertations

Page generated in 0.0023 seconds