[PDF] Bayesian Variable Selection in Normal Regression Models

What is the Bayesian method of variable selection?

The Bayesian approach to variable selection is straightforward in principle.
One quantifies the prior uncertainties via probabilities for each model under consideration, specifies a prior distribution for each of the parameters in each model, and then uses Bayes' theorem to calculate posterior model probabilities.

What are the methods of variable selection in regression modeling?

Outlined below are four major variable selection methods: backward elimination, forward selection, stepwise selection and all possible subset selection, and a discussion of their pros and cons.

How do you decide which variables to include in regression?

When building a linear or logistic regression model, you should consider including:

How do you decide which variables to include in regression?

Bayesian regression employs prior belief or knowledge about the data to “learn” more about it and create more accurate predictions.
It also takes into account the data's uncertainty and leverages prior knowledge to provide more precise estimates of the data.