.. _example_linear_model_plot_bayesian_ridge.py: ========================= Bayesian Ridge Regression ========================= Computes a Bayesian Ridge Regression on a synthetic dataset. See :ref:`bayesian_ridge_regression` for more information on the regressor. Compared to the OLS (ordinary least squares) estimator, the coefficient weights are slightly shifted toward zeros, which stabilises them. As the prior on the weights is a Gaussian prior, the histogram of the estimated weights is Gaussian. The estimation of the model is done by iteratively maximizing the marginal log-likelihood of the observations. .. rst-class:: horizontal * .. image:: images/plot_bayesian_ridge_001.png :scale: 47 * .. image:: images/plot_bayesian_ridge_002.png :scale: 47 * .. image:: images/plot_bayesian_ridge_003.png :scale: 47 **Python source code:** :download:`plot_bayesian_ridge.py ` .. literalinclude:: plot_bayesian_ridge.py :lines: 19- **Total running time of the example:** 0.26 seconds ( 0 minutes 0.26 seconds)