What is the difference between alpha and lambda in linear_model.BayesianRidge() of sklearn?
I would like to estimate a linear regression y = w_0 + w_1 x_1 + w_2 x_2 + e with priors for w_0, w_1, w_2 to be normally distributed. w_0 = N(0, sigma0), w1 = N(0, sigma1) and w_2 = N(0, sigma2).
reg = linear_model.BayesianRidge(alpha_1=sigma0, fit_intercept=True, lambda_1=sigma0, normalize=False)
