I am having a hard time understanding the Bayesian Ridge Regression.
In Bayesian Ridge Regression, is the ridge regression parameter $\lambda$ treated as random? or do we treat the $\lambda$ as given?
Thank you,
I am having a hard time understanding the Bayesian Ridge Regression.
In Bayesian Ridge Regression, is the ridge regression parameter $\lambda$ treated as random? or do we treat the $\lambda$ as given?
Thank you,
In Bayesian approach we treat every parameter as a random variable and define a prior for it. The prior is defined in terms of some parameters, e.g. $\boldsymbol{\beta} \sim \mathcal{N}(0, \boldsymbol{\Lambda}^{-1})$, where the parameters of the priors are either chosen in advance, or we assume priors for them as well, and estimate them from the data.