Understanding the Theory of Bayesian Regression
Understand Bayesian regression theory by exploring how posterior distributions are derived using Bayes’ theorem. Learn to model uncertainties in regression parameters and implement Bayesian regression in Python. Gain foundational skills to apply probabilistic regression methods for more robust machine learning predictions.
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Bayesian regression
Bayesian theory is a probabilistic approach to statistical inference and is widely used in machine learning for various tasks, including regression. Bayesian regression involves modeling a relationship between a response variable and one or more predictor variables while considering the uncertainty in the model parameters.
Mathematical foundation
In Bayesian regression, the goal is to infer the posterior distribution of the model parameters given the observed data. Bayes’ theorem gives the posterior distribution:
Where:
is the posterior distribution of the parameters ...