Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

This article is concerned with the selection of subsets of predictor variables in a linear regression model for the prediction of a dependent variable. It is based on a Bayesian approach, intended to ...

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols, Bayesian log-Gaussian Cox process regression, ...

Bayesian Quantile Regression and Statistical Modelling Publication Trend The graph below shows the total number of publications each year in Bayesian Quantile Regression and Statistical Modelling.

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior and predictive ...