  We develop a fully automatic Bayesian Lasso via variational inference. This
is a scalable procedure for approximating the posterior distribution. Special
attention is driven to the knot selection in regression spline. In order to
carry through our proposal, a full automatic variational Bayesian Lasso, a
Jefferey's prior is proposed for the hyperparameters and a decision theoretical
approach is introduced to decide if a knot is selected or not. Extensive
simulation studies were developed to ensure the effectiveness of the proposed
algorithms. The performance of the algorithms were also tested in some real
data sets, including data from the world pandemic Covid-19. Again, the
algorithms showed a very good performance in capturing the data structure.
