# linear regression

## Multiple Linear Regression (JAGS)

This tutorial will focus on the use of Bayesian estimation to fit simple linear regression models. BUGS (Bayesian inference Using Gibbs Sampling) is an algorithm and supporting language (resembling R) dedicated to performing the Gibbs sampling implementation of Markov Chain Monte Carlo (MCMC) method.

## Multiple Linear Regression (STAN)

This tutorial will focus on the use of Bayesian estimation to fit simple linear regression models. BUGS (Bayesian inference Using Gibbs Sampling) is an algorithm and supporting language (resembling R) dedicated to performing the Gibbs sampling implementation of Markov Chain Monte Carlo (MCMC) method.

## Simple Linear Regression (JAGS)

This tutorial will focus on the use of Bayesian estimation to fit simple linear regression models. BUGS (Bayesian inference Using Gibbs Sampling) is an algorithm and supporting language (resembling R) dedicated to performing the Gibbs sampling implementation of Markov Chain Monte Carlo (MCMC) method.

## Simple Linear Regression (STAN)

This tutorial will focus on the use of Bayesian estimation to fit simple linear regression models. BUGS (Bayesian inference Using Gibbs Sampling) is an algorithm and supporting language (resembling R) dedicated to performing the Gibbs sampling implementation of Markov Chain Monte Carlo (MCMC) method.

## Super basic introduction to OpenBUGS

The focus of this simple tutorial is to provide a brief introduction and overview about how to fit Bayesian models using OpenBUGS via R. Prerequisites: The latest version of R, which can be downloaded and installed for Windows, Mac or Linux OS from the CRAN website I also strongly recommend to download and install Rstudio, an integrated development environment which provides an “user-friendly” interaction with R (e.

## Super basic introduction to JAGS

The focus of this simple tutorial is to provide a brief introduction and overview about how to fit Bayesian models using JAGS via R. Prerequisites: The latest version of R, which can be downloaded and installed for Windows, Mac or Linux OS from the CRAN website I also strongly recommend to download and install Rstudio, an integrated development environment which provides an “user-friendly” interaction with R (e.

## Super basic introduction to STAN

The focus of this simple tutorial is to provide a brief introduction and overview about how to fit Bayesian models using STAN via R. Prerequisites: The latest version of R, which can be downloaded and installed for Windows, Mac or Linux OS from the CRAN website I also strongly recommend to download and install Rstudio, an integrated development environment which provides an “user-friendly” interaction with R (e.