R code for parameter estimates in regression models with manual implementation of least squares, gradient descent and monte carlo methods.
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README.md
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script_regression_parameters.R
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univariate_simple_example.r

README.md

Regression parameter estimates

R code for parameter estimates in regression models using different methods:

  1. Least squares
  2. Gradient descent
  3. Metropolis-Hastings
  4. Gibbs sampling using JAGS

The code is for a linear regression problem with one single predictor (univariate regression). The aim is to introduce important aspects widely used in machine learning, such as gradient descent and Monte Carlo methods, using a simple example and providing basic implementations for all methods.

The different approaches and code are explained in this blog post: http://www.marcoaltini.com/blog/parameter-estimates-for-regression-least-squares-gradient-descent-and-monte-carlo-methods